Table of Contents

• The INNS President's WelcomeDonald C. Wunsch. [doi]
• Exploring chemical space with computers: challenges and opportunitiesPierre Baldi. 83 [doi]
• Automatic target recognition using new support vector machineDavid P. Casasent, Yu-Chiang Wang 0001. 84-89 [doi]
• Training with heterogeneous dataJohn A. Drakopoulos, Ahmad Abdulkader. 90-95 [doi]
• Tracking the states of a nonlinear system in the weight-space of a feed-forward neural networkTakahiro Emoto, Masatake Akutagawa, Udantha R. Abeyratne, Hirofumi Nagashino, Yohsuke Kinouchi. 96-101 [doi]
• Data-dependent kernels for high-dimensional data classificationJingdong Wang 0001, James T. Kwok, Helen C. Shen, Long Quan. 102-107 [doi]
• Generalized 2D principal component analysisHui Kong 0001, Xuchun Li, Lei Wang 0001, Earn Khwang Teoh, Jian-Gang Wang, Ronda Venkateswarlu. 108-113 [doi]
• Optimal gradient-based learning using importance weightsSepp Hochreiter, Klaus Obermayer. 114-119 [doi]
• Cinematographic construction by brains of knowledge from informationWalter J. Freeman. 120-124 [doi]
• Analysis of phase transitions in KIV with amygdala during simulated navigation controlRobert Kozma 0001, Mark Myers. 125-130 [doi]
• Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interactionBarani Raman, Ricardo Gutierrez-Osuna. 131-136 [doi]
• Evolving neurodynamic controllers for autonomous robotsDerek Harter. 137-142 [doi]
• Data visualization methodologies for data mining systems in bioinformaticsAntonino Staiano, Angelo Ciaramella, Giancarlo Raiconi, Roberto Tagliaferri, Roberto Amato, Giuseppe Longo, Gennaro Miele, Ciro Donalek. 143-148 [doi]
• Random projections for assessing gene expression cluster stabilityAlberto Bertoni, Giorgio Valentini. 149-154 [doi]
• A new approach to hierarchical clustering for the analysis of genomic dataFrancesco Masulli, Stefano Rovetta. 155-160 [doi]
• Inferring protein-protein interactions using interaction network topologiesAlberto Paccanaro, Valery Trifonov, Haiyuan Yu, Mark Gerstein. 161-166 [doi]
• Predicting sugar regulation in Arabidopsis thaliana using kernel learning methodsKamel Saadi, Kee Khoon Lee, Gavin C. Cawley, Michael W. Bevan. 167-172 [doi]
• Feedback linearization using neural networks applied to advanced pharmacodynamic and pharmacogenomic systemsAlexandru G. Floares. 173-178 [doi]
• Improved spam e-mail filtering based on committee machines and information theoretic feature extractionVasilis Zorkadis, M. Panayotou, Dimitrios A. Karras. 179-184 [doi]
• A variational Bayesian method for rectified factor analysisMarkus Harva, Ata Kabán. 185-190 [doi]
• An information theoretic approach to adaptive system training using unlabeled dataKyu-Hwa Jeong, Jian-Wu Xu, José C. Príncipe. 191-195 [doi]
• A study of AdaBoost with SVM based weak learnersXuchun Li, Lei Wang 0001, Eric Sung. 196-201 [doi]
• Information maximization and cost minimization in information-theoretic competitive learningRyotaro Kamimura. 202-207 [doi]
• Maximally discriminative spectral feature projections using mutual informationUmut Ozertem, Deniz Erdogmus. 208-213 [doi]
• Extending kernel principal component analysis to general underlying loss functionsCarlos Alzate, Johan A. K. Suykens. 214-219 [doi]
• Nonlinearity and optimal component analysisWashington Mio, Qiang Zhang, Xiuwcn Liu. 220-225 [doi]
• Relation between kernel CCA and kernel FDAMakoto Yamada, Ali Pezeshki, Mahmood R. Azimi-Sadjadi. 226-231 [doi]
• Extraction of frame-difference features based on PCA and ICA for lip-readingKyungsuk David Lee, Michelle Jeungeun Lee, Soo-Young Lee. 232-237 [doi]
• Multinomial PCA for extracting major latent topics from document streamsMasahiro Kimura, Kazumi Saito, Naonori Ueda. 238-243 [doi]
• Post nonlinear blind source separation by geometric linearizationThang Viet Nguyen, Jagdish Chandra Patra, Amitabha Das, Geok See Ng. 244-249 [doi]
• Which features trigger action potentials in cortical neurons in vivo?Holger Fröhlich, Björn Naundorf, Maxim Volgushev, Fred Wolf. 250-255 [doi]
• Attention as sigma-pi controlled ACh-based feedbackJohn G. Taylor, Matthew Hartley, Neill R. Taylor. 256-261 [doi]
• Motivational modulation of endogenous inputs to the superior colliculusJason Satel, Thomas Trappenberg, Raymond M. Klein. 262-267 [doi]
• Facilitatory neural activity compensating for neural delays as a potential cause of the flash-lag effectHeejin Lim, Yoonsuck Choe. 268-273 [doi]
• Fisher information quantifies task-specific performance in the blowfly photoreceptorPeng Xu 0009, Pamela Abshire. 274-279 [doi]
• Evolutionary training of a biologically realistic spino-neuromuscular systemStanley Gotshall, Christopher Canine, Benjamin Jennings, Terence Soule. 280-285 [doi]
• Gene regulatory networks inference with recurrent neural network modelsRui Xu 0001, Donald C. Wunsch II. 286-291 [doi]
• A self-organizing neural network approach for the identification of motifs with insertions and deletions in protein sequencesXiaoxu Xiong, Derong Liu 0001, Huaguang Zhang. 292-297 [doi]
• Functional grouping of genes using spectral clustering and Gene OntologyNora Speer, Holger Fröhlich, Christian Spieth, Andreas Zell. 298-303 [doi]
• A simpler Bayesian network model for genetic regulatory network inferenceGustavo Bastos, Katia S. Guimarães. 304-309 [doi]
• Effect of non-target examples on E.coli promoters recognition using neural networksPaul C. Conilione, Dianhui Wang. 310-315 [doi]
• Predicting protein-protein interactions based on protein-domain relationshipsBing Wang 0004, De-Shuang Huang, Peng Chen 0001, Yunping Zhu, Yixue Li. 316-319 [doi]
• Automated heuristic growing of neural networks for nonlinear time series modelsAlex Kalos. 320-325 [doi]
• ECG signal classification using block-based neural networksWei Jiang, Seong G. Kong, Gregory D. Peterson. 326-331 [doi]
• An evolved Seega player capable of strong novice-level playAshraf M. Abdelbar, Ossama Soliman, Sherif Kinawy, Hisham Sayed. 332-336 [doi]
• Evolvable neural networks based on developmental models for mobile robot navigationDong-Wook Lee, Seong G. Kong, Kwee-Bo Sim. 337-342 [doi]
• A recurrent RBF network model for nearest neighbor classificationMehmet K. Muezzinoglu, Jacek M. Zuracla. 343-348 [doi]
• Face recognition using modular neural networks and fuzzy Sugeno integral for response integrationPatricia Melin, Claudia I. González, Felma Gonzalez, Oscar Castillo 0001. 349-354 [doi]
• Application of a CMAC neural network to the control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicleFrederick G. Harmon, Andrew A. Frank, Sanjay S. Joshi. 355-360 [doi]
• Inverse optimal nonlinear recurrent high order neural observerLuis J. Ricalde, Edgar N. Sánchez. 361-365 [doi]
• System and method for determining harmonic contributions from non-linear loads using recurrent neural networksJoy Mazumdar, Ronald G. Harley, Franklin C. Lambert. 366-371 [doi]
• On output regulation for SISO nonlinear systems with dynamic neural networksBernardino Castillo-Toledo, A. Hernández Avalos. 372-377 [doi]
• Indirect field-oriented linear induction motor drive with Petri fuzzy-neural-network controlRong-Jong Wai, Chia-Chin Chu. 378-383 [doi]
• Wavelet-neural-network-based backstepping control for chaotic systemsTsu-Tian Lee, Chih-Min Lin, Chun-Fei Hsu. 384-389 [doi]
• Spatially and temporally local spike-timing-dependent plasticity ruleAnatoli Gorchetchnikov, Massimiliano Versace, Michael E. Hasselmo. 390-395 [doi]
• Differences in the subthreshold dynamics of leaky integrate-and-fire and Hodgkin-Huxley neuron modelsDominic I. Standage, Thomas Trappenberg. 396-399 [doi]
• Rich phenomena of pulse-coupled spiking neurons with triangular waveform inputToshimichi Saito, Yoshio Kon'no, Hiroyuki Torikai. 400-404 [doi]
• Synchronized theta rhythm selection in a dentate gyrus network modelKatsumi Tateno, Hatsuo Hayashi, Satoru Ishizuka. 405-409 [doi]
• Noise benefits in spiking retinal and sensory neuron modelsAshok Patel, Bart Kosko. 410-415 [doi]
• A spiking neuron representation of auditory signalsGuoping Wang, Misha Pavel. 416-421 [doi]
• Comparative genomic study of Parkinson's disease candidate genesGavyn Pang, Jagath C. Rajapakse. 422-427 [doi]
• NeuroGene: integrated simulation of gene regulation, neural activity and neurodevelopmentRasmus Storjohann, Gary F. Marcus. 428-433 [doi]
• A hierarchical coevolutionary method to support brain-lesion modellingMichail Maniadakis, Panos E. Trahanias. 434-439 [doi]
• Genome space and structure genome invariantsGermano Resconi. 440-445 [doi]
• A computational neurogenetic model of a spiking neuronNikola K. Kasabov, Lubica Benuskova, Simei Gomes Wysoski. 446-451 [doi]
• Self-organizing hierarchical knowledge discovery by an ARTMAP information fusion systemGail A. Carpenter, Siegfried Martens. 452-456 [doi]
• Modification of the ART-1 architecture based on category theoretic design principlesMichael J. Healy, Richard D. Olinger, Robert J. Young, Thomas P. Caudell, Kurt W. Larson. 457-462 [doi]
• A vigilance-free ART network with general geometry internal categoriesDinani Gomes, Manuel Fernández Delgado, Senén Barro. 463-468 [doi]
• On the design of an ellipsoid ARTMAP classifier within the fuzzy adaptive system ART frameworkRoss Peralta, Georgios C. Anagnostopoulos, Eduardo Gómez-Sánchez, Samuel Richie. 469-474 [doi]
• Parallelizing the fuzzy ARTMAP algorithm on a Beowulf clusterJimmy Secretan, José Castro, Michael Georgiopoulos, Joe Tapia, Amit Chadha, Brian Huber, Georgios C. Anagnostopoulos, Samuel Richie. 475-480 [doi]
• Using real-valued meta classifiers to integrate binding site predictionsYi Sun 0001, Mark Robinson, Rod Adams, Paul Kaye, Alistair G. Rust, Neil Davey. 481-486 [doi]
• Cluster ensemble for gene expression microarray dataMarcilio C. P. de Souto, Shirly C. M. Silva, Valnaide G. Bittencourt, Daniel S. A. de Araujo. 487-492 [doi]
• Characterizing human gene splice sites using evolved regular expressionsJing-jing Li, De-Shuang Huang, Robert M. MacCallum, Xiao-Run Wu. 493-498 [doi]
• Prediction of contact map integrated PNN with conformational energyPeng Chen 0001, De-Shuang Huang, Bing Wang 0004, Yunping Zhu, Yixue Li. 499-502 [doi]
• Neural network-based analysis of DNA microarray dataJagdish Chandra Patra, Lei Wang, Ee-Luang Ang, Narendra S. Chaudhari. 503-508 [doi]
• Neural networks for gene expression analysis and gene selection from DNA microarrayJagdish Chandra Patra, Qin Zhen, Ee-Luang Ang, Amitabha Das. 509-514 [doi]
• The molecules module of the brain architecture management systemMihail Bota, Larry W. Swanson. 515-520 [doi]
• Protein flexibility modeling using kernel based methodsXue-wen Chen, Jeremy Chen. 521-526 [doi]
• An empirical comparison of individual machine learning techniques and ensemble approaches in protein structural class predictionValnaide G. Bittencourt, Márjory C. C. Abreu, Marcilio C. P. de Souto, Anne Magály de Paula Canuto. 527-531 [doi]
• Protein secondary structure prediction using machine learningBaifang Zhang, ZhiHang Chen, Yi Lu Murphey. 532-537 [doi]
• Protein secondary structure prediction with a hybrid RNN/HMM systemJinmiao Chen, Narendra S. Chaudhari. 538-541 [doi]
• Entropy based disease classification of proteomic mass spectrometry data of the human serum by a support vector machineTerje Kristensen, Gaurav Kumar. 542-545 [doi]
• A comparative study on term weighting schemes for text categorizationMan Lan, Sam Yuan Sung, Hwee-Boon Low, Chew Lim Tan. 546-551 [doi]
• One class support vector machine based non-relevance feedback document retrievalTakashi Onoda, Hiroshi Murata, Seiji Yamada. 552-557 [doi]
• Text clustering with NTSO (neural text self organizer)Taeho Jo 0001, Nathalie Japkowicz. 558-563 [doi]
• A neuro-SVM model for text classification using latent semantic indexingVikramjit Mitra, Chia-Jiu Wang, Satarupa Banerjee. 564-569 [doi]
• Fast text categorization with min-max modular support vector machinesFeng-Yao Liu, Ke Wu, Hai Zhao 0001, Bao-Liang Lu. 570-575 [doi]
• Weight sharing on naive Bayes document modelKazumi Saito, Ryohei Nakano. 576-581 [doi]
• Optimization of parameters for effective Web information retrieval using an evolutionary algorithmJohn Zakos, Ping Zhang 0008, Brijesh K. Verma. 582-587 [doi]
• Snap-drift learning for phrase recognitionSin Wee Lee, Dominic Palmer-Brown. 588-592 [doi]
• An adaptive function neural network (ADFUNN) for phrase recognitionMiao Kang, Dominic Palmer-Brown. 593-597 [doi]
• Treatment of missing data using neural networks and genetic algorithmsMussa Abdella, Tshilidzi Marwala. 598-603 [doi]
• An iterative relevance feedback learning algorithm for image retrieval systemsSaravanakumar Srinivasan, Mahmood R. Azimi-Sadjadi. 604-609 [doi]
• Design of an optical fixed-weight learning neural networkArthur Steven Younger, Emmett Redd. 610-615 [doi]
• Forgetful logic circuits for pulse-mode neural networksRichard B. Wells, Anindya Bhattacharya, Ben Sharon, Priyank Gupta, Sam Young, Sanjeev Giri, Terseer Ityavyar, Dave Cox. 616-621 [doi]
• Artificial neural network computation on graphic process unitZhongwen Luo, Hongzhi Liu, Xincai Wu. 622-626 [doi]
• Circuit implementation of multi-thresholded neuron (MTN) using BiCMOS technologyXiaolei Zhu, Jizhong Shen, Baoyong Chi, Zhihua Wang 0001. 627-632 [doi]
• FPGA implementation of pulse coupled oscillatorYutaka Maeda, Makito Nakatsuka. 633-638 [doi]
• Analog current-mode CMOS implementation of central pattern generator for robot locomotionKazuki Nakada, Tetsuya Asai, Tetsuya Hirose, Yoshihito Amemiya. 639-644 [doi]
• Toward an analog VLSI implementation of a decision making modelYili Quan, Albert H. Titus. 645-650 [doi]
• On Kolmogorov's superpositions: novel gates and circuits for nanoelectronics?Valeriu Bern, Artur Zawadzki. 651-656 [doi]
• Hardware implementation of CMAC and B-spline neural networks for embedded applicationsQiuye Zhao, Donald S. Reay. 657-662 [doi]
• DSP-based neural systems for the perceptual assessment of visual qualityPaolo Gastaldo, Giovanni Parodi, Rodolfo Zunino. 663-668 [doi]
• Analog current-mode design for soft-max computationDavide Piombo, Rodolfo Zunino. 669-674 [doi]
• Analog current-mode design for soft-max computationDavide Piombo, Rodolfo Zunino. 669-674 [doi]
• Low-voltage pseudo floating-gate reconfigurable linear threshold elementsØivind Næss, Snorre Aunet, Yngvar Berg. 675-680 [doi]
• The effect of quantization on support vector machines with Gaussian kernelDavide Anguita, Giovanni Bozza. 681-684 [doi]
• Building a cheaper artificial brainHugo de Garis, Wang Ce, Thayne Batty. 685-688 [doi]
• A digital LSI architecture of elastic graph matching and its FPGA implementationTeppei Nakano, Takashi Morie. 689-694 [doi]
• Simulated control of a tracking mobile robot by four aVLSI integrate-and-fire neurons paired into mapsJeffrey Dungen, Jean-Jules Brault. 695-699 [doi]
• Recurrent neural networks training with stable risk-sensitive Kalman filter algorithmWen Yu 0001, José de Jesús Rubio, Xiaoou Li. 700-705 [doi]
• A method of oscillatory trajectory generation using recurrent hybrid neural networksYasuaki Kuroe, Kei Miura. 706-711 [doi]
• A new k-winners-take-all neural networkShubao Liu, Jun Wang. 712-716 [doi]
• A constrained optimization algorithm for training locally recurrent globally feedforward neural networksParis A. Mastorocostas. 717-722 [doi]
• SCRAM: statistically converging recurrent associative memorySylvain Chartier, Sébastien Hélie, Mounir Boukadoum, Robert Proulx. 723-728 [doi]
• A new model for learning in graph domainsMarco Gori, Gabriele Monfardini, Franco Scarselli. 729-734 [doi]
• Empirical approximation for Lyapunov functions with artificial neural netsGürsel Serpen. 735-740 [doi]
• Ability to skip steps emerging from chaotic dynamicsLuciana P. P. Bueno, Aluízio F. R. Araújo. 741-746 [doi]
• Nonlinear dynamics on VSF-networkYoshitsugu Kakemoto, Shinichi Nakasuka. 747-752 [doi]
• Dynamical behavior of a chaotic neural and its application to optimization problemsToshijiro Tanaka, Etsumasa Hiura. 753-757 [doi]
• An analysis of associative chaotic neurodynamics by using surrogate neuronsMasaharu Adachi. 758-763 [doi]
• A new kind of Hopfield networks for finding global optimumXiaofei Huang. 764-769 [doi]
• Learning cycles brings chaos in Hopfield networksColin Molter, Utku Salihoglu, Hugues Bersini. 770-775 [doi]
• Phase diagrams for locally Hopfield neural networks in presence of correlated patternsFilip Piekniewski, Tomasz Schreiber. 776-781 [doi]
• 2 types of complex-valued Hopfield networks and the application to a traffic signal controlIkuko Nishikawa, Kazutoshi Sakakibara, Takeshi Iritani, Yasuaki Kuroe. 782-787 [doi]
• A new N-parallel updating method of the Hopfield-type neural network for N-queens problemThanh-Nhat Le, Cong-Kha Pham. 788-791 [doi]
• On-line system identification using context discernmentLars Holmstrom, Roberto A. Santiago, George G. Lendaris. 792-797 [doi]
• The k-server problem: a reinforcement learning approachManoel Leandro de Lima Junior, Adrião D. D. Neto, Jorge Dantas de Melo. 798-802 [doi]
• Task similarity measures for transfer in reinforcement learning task librariesJames L. Carroll, Kevin D. Seppi. 803-808 [doi]
• Neural network model for time series prediction by reinforcement learningFeng Liu 0043, Chai Quek, Geok See Ng. 809-814 [doi]
• Learning nonlinear state-space models for controlTapani Raiko, Matti Tornio. 815-820 [doi]
• A neural network model for the decision-making process based on AHPSatoshi Matsuda. 821-826 [doi]
• Negative reinforcement and backtrack-points for recurrent neural networks for cost-based abductionAshraf M. Abdelbar, Mostafa A. El-Hemaly, Emad A. M. Andrews, Donald C. Wunsch II. 827-832 [doi]
• Distributed computation for neural-based abductive reasoningLotfi Ben Romdhane, Mourad Elhadef. 833-838 [doi]
• Transductive modeling with GA parameter optimizationNisha Mohan, Nikola K. Kasabov. 839-844 [doi]
• Symbolic rule extraction with a scaled conjugate gradient version of CLARIONTasos Falas, Andreas Stafylopatis. 845-848 [doi]
• One-against-all multi-class SVM classification using reliability measuresYi Liu, Yuan F. Zheng. 849-854 [doi]
• K-fold generalization capability assessment for support vector classifiersDavide Anguita, Sandro Ridella, Fabio Rivieccio. 855-858 [doi]
• Weighted support vector machine for data classificationXuLei Yang, Qing Song 0001, Aizo Cao. 859-864 [doi]
• Algorithms of fast SVM evaluation based on subspace projectionJian-xiong Dong, Ching Y. Suen, Adam Krzyzak. 865-870 [doi]
• Yet faster method to optimize SVR hyperparameters based on minimizing cross-validation errorKenji Kobayashi, Daisuke Kitakoshi, Ryohei Nakano. 871-876 [doi]
• Support vector regression performance analysis and systematic parameter selectionPao-Tsun Lin, Shun-Feng Su, Tsu-Tian Lee. 877-882 [doi]
• Pre-selection of working set for SVM decomposition algorithmXuLei Yang, Qing Song 0001, Sheng Liu. 883-888 [doi]
• The intermediate matching kernel for image local featuresSabri Boughorbel, Jean-Philippe Tarel, Nozha Boujemaa. 889-894 [doi]
• MIMO SVMs for classification and regression using the geometric algebra frameworkEduardo Bayro-Corrochano, Nancy Arana-Daniel. 895-900 [doi]
• Fuzzy integral for a rapid mixture of support vector machinesHassiba Nemmour, Youcef Chibani. 901-906 [doi]
• Feature subset selection for support vector machines using confident marginMauricio Kugler, Kazuma Aoki, Susumu Kuroyanagi, Akira Iwata, Anto Satriyo Nugroho. 907-912 [doi]
• Assignment kernels for chemical compoundsHolger Fröhlich, Jörg K. Wegner, Andreas Zell. 913-918 [doi]
• Characterization of data complexity for SVM methodsYunqian Ma, Vladimir Cherkassky. 919-924 [doi]
• Optimizing resources in model selection for support vector machinesMathias M. Adankon, Mohamed Cheriet, Nedjem-Eddine Ayat. 925-930 [doi]
• A convergence rate estimate for the SVM decomposition methodDaniel T. H. Lai, Alistair Shilton, Nallasamy Mani, Marimuthu Palaniswami. 931-936 [doi]
• Deriving kernels from MLP probability estimators for large categorization problemsIvan Titov, James Henderson 0001. 937-942 [doi]
• SVM ensembles for selecting the relevant feature subsetsTao Ban, Shigeo Abe. 943-948 [doi]
• Pattern de-noising based on support vector data descriptionJooyoung Park 0001, Daesung Kang, Jongho Kim, James Tin-Yau Kwok, Ivor Wai-Hung Tsang. 949-953 [doi]
• Kernel relevant component analysis for distance metric learningIvor Wai-Hung Tsang, Pak-Ming Cheung, James Tin-Yau Kwok. 954-959 [doi]
• A new saliency measure for inputs selection and node pruning in neural networkEric Fock, Alfred Jean Philippe Lauret, Thierry Alex Mara. 960-965 [doi]
• One-step neural network inversion with PDF learning and emulationLeemon Baird, David Smalenberger, Shawn Ingkiriwang. 966-971 [doi]
• Effective neural network pruning using cross-validationThuan Q. Huynh, Rudy Setiono. 972-977 [doi]
• An efficient learning algorithm for finding multiple solutions based on fixed-point homotopy methodHiroshi Ninomiya, Chikahiro Tomita, Hideki Asai. 978-983 [doi]
• An analysis of underfitting in MLP networksSridhar Narayan, Gene A. Tagliarini. 984-988 [doi]
• Experimental evaluation of a hybrid method for configuring ensemble encoding receptorsAshraf M. Abdelbar, Deena Hassan, Gene A. Tagliarini, Sridhar Narayan. 989-994 [doi]
• Dynamic construction of fault tolerant multi-layer neural networksHaruhiko Takase, Ayumi Nobuto, Hidehiko Kita, Terumine Hayashi. 995-999 [doi]
• The p-Center machineMichael Brückner. 1000-1005 [doi]
• A better scaled local tangent space alignment algorithmJian Yang, Fuxin Li, Jue Wang. 1006-1011 [doi]
• Gaussian processes of nonlinear diffusion filteringRamunas Girdziusas, Jorma Laaksonen. 1012-1013 [doi]
• Fast training of multilayer perceptrons with a mixed norm algorithmSabeur Abid, Farhat Fnaiech, Barrie W. Jervis, Mohamed Cheriet. 1018-1022 [doi]
• Decorrelating parametrical neural networkBoris V. Kryzhanovsky, Vladimir Kryzhanovsky, Anatoly B. Fonarev. 1023-1026 [doi]
• Second-order backpropagation algorithms for a stagewise-partitioned separable Hessian matrixEiji Mizutani, Stuart E. Dreyfus, James Weldon Demmel. 1027-1032 [doi]
• Prediction of time to event for censored data: ridge regression with linear constraints in kernel spaceNatasha Bagotskaya, Ilia Lossev, Ninel Losseva, Mikhail Parakhin. 1033-1038 [doi]
• A threshold varying bisection method for cost sensitive learning in neural networksParag C. Pendharkar. 1039-1044 [doi]
• A single-layer radial basis function network classifier and its applicationsDaqi Gao, Mingming Chen, Yongli Li. 1045-1050 [doi]
• OLS versus SVM approach to learning of RBF networksStanislaw Osowski, Tomasz Markiewicz. 1051-1056 [doi]
• Designing RBF classifiers for weighted boostingVanessa Gómez-Verdejo, Jerónimo Arenas-García, Manuel Ortega-Moral, Aníbal R. Figueiras-Vidal. 1057-1062 [doi]
• Iterative feature weighting for identification of relevant features with radial basis function networksBaofu Duan, Yoh-Han Pao. 1063-1068 [doi]
• Sequential neuron pruning algorithm for RBF network with guaranteed stabilityJie Ni, Qing Song 0001. 1069-1074 [doi]
• Factors of overtraining with fuzzy ARTMAP neural networksPhilippe Henniges, Eric Granger, Robert Sabourin. 1075-1080 [doi]
• ART2 based classification of sparse high dimensional parameter sets for a simulation parameter selection assistantGregory A. Klotz, Deborah A. Stacey. 1081-1085 [doi]
• Fuzzy PSO: a generalization of particle swarm optimizationAshraf M. Abdelbar, Suzan Abdelshahid, Donald C. Wunsch II. 1086-1091 [doi]
• Simulating artificial life using Boltzmann machinesLeszek Rybicki. 1092-1097 [doi]
• Artificial cognitive BP-CT ant routing algorithmJing Xu, Chunyu Liu, Xiaobo Sun. 1098-1103 [doi]
• Basic property of a quantum neural network composed of Kane's qubitsYuuki Nakamiya, Mitsunaga Kinjo, Osamu Takahashi, Shigeo Sato, Koji Nakajima. 1104-1107 [doi]
• Quantum gauged neural networks: learning and recallingYukari Fujita, Takashi Hiramatsu, Tetsuo Matsui. 1108-1113 [doi]
• Stochastic complexity of variational Bayesian hidden Markov modelsTikara Hosino, Kazuho Watanabe, Sumio Watanabe. 1114-1119 [doi]
• New experiments on ensembles of multilayer feedforward for classification problemsCarlos Hernández-Espinosa, Joaquín Torres-Sospedra, Mercedes Fernández-Redondo. 1120-1124 [doi]
• A research on combination methods for ensembles of multilayer feedforwardJoaquín Torres-Sospedra, Mercedes Fernández-Redondo, Carlos Hernández-Espinosa. 1125-1130 [doi]
• Dynamically weighted majority voting for incremental learning and comparison of three boosting based approachesAliasgar Gangardiwala, Robi Polikar. 1131-1136 [doi]
• A comparison of combination methods for ensembles of RBF networksJoaquín Torres-Sospedra, Carlos Hernández-Espinosa, Mercedes Fernández-Redondo. 1137-1141 [doi]
• New boosting methods of Gaussian processes for regressionYangqiu Song, Changshui Zhang. 1142-1147 [doi]
• Learning probability density functions from marginal distributions with applications to Gaussian mixturesQutang Cai 0001, Changshui Zhang, Chunyi Peng 0001. 1148-1153 [doi]
• Evaluation of cluster combination functions for mixture of expertsRobert Redhead, Malcolm Heywood. 1154-1159 [doi]
• Mixture of heterogeneous experts applied to time series: a comparative studyWilfredo Jaime Puma Villanueva, Clodoaldo Ap. M. Lima, Euripedes P. dos Santos, Fernando J. Von Zuben. 1160-1165 [doi]
• Classification and verification through the combination of the multi-layer perceptron and auto-association neural networksAlexander Iversen, Nicholas K. Taylor, Keith E. Brown. 1166-1171 [doi]
• A classifier ensemble model and its applicationsDaqi Gao, Shangming Zhu, Wei Chen, Yongli Li. 1172-1177 [doi]
• Systematic rewiring in associative neural networks with small-world architectureOleksiy Dekhtyarenko. 1178-1181 [doi]
• Motion perception with recurrent self-organizing maps based modelsVolker Baier. 1182-1186 [doi]
• Retrieval property of associative memory with negative resistanceYoshihiro Hayakawa, Hongge Li, Koji Nakajima. 1187-1192 [doi]
• A weighted voting model of associative memory: theoretical analysisXiaoyan Mu, Paul Watta, Mohamad H. Hassoun. 1193-1198 [doi]
• New results on binary auto- and heteroassociative morphological memoriesPeter Sussner. 1199-1204 [doi]
• Self-organizing neural grove and its applicationsHirotaka Inoue, Hiroyuki Narihisa. 1205-1210 [doi]
• An incremental growing neural gas learns topologiesYann Prudent, Abdellatif Ennaji. 1211-1216 [doi]
• Robust continuous learning in a WTA neural network for clustering symbol stringsJohn A. Flanagan. 1217-1222 [doi]
• An associative memory for the on-line recognition and prediction of temporal sequencesJoy Bose, Steve B. Furber, Jonathan L. Shapiro. 1223-1228 [doi]
• Similar-image retrieval systems using ICA and PCA basesNaoto Katsumata, Yasuo Matsuyama. 1229-1234 [doi]
• Blind inversion of Wiener system for single source using nonlinear blind source separationZhan-Li Sun, De-Shuang Huang, Chun-Hou Zheng 0001, Li Shang. 1235-1238 [doi]
• Blind separation of convolved sources using the information maximization approachMd. Hasanuzzaman, K. Khrosani. 1239-1244 [doi]
• BSS Toolbox for delayed and convolved mixturesAngelo Ciaramella, Roberto Tagliaferri, Francesco Iorio. 1245-1250 [doi]
• Diagonally weighted and shifted criteria for minor and principal component extractionMohammed A. Hasan. 1251-1256 [doi]
• Analysis of signal separation and signal distortion in feedforward and feedback blind source separation based on source spectraAkihide Horita, Kenji Nakayama, Akihiro Hirano, Yasuhiro Dejima. 1257-1262 [doi]
• Weighted Rayleigh quotients for minor and principal component extractionMohammed A. Hasau. 1263-1268 [doi]
• Feature ranking using supervised neural gas and informational energyRazvan Andonie, Angel Cataron. 1269-1273 [doi]
• An improved kernel Fisher discriminant classifier and its applicationsDaqi Gao, Zhen Wang, Yongli Li. 1274-1279 [doi]
• An optimal entropy estimator for discrete random variablesMotoki Shiga, Yasunari Yokota. 1280-1285 [doi]
• Comparing "pattern discovery" and back-propagation classifiersAndrew Hamilton-Wright, Daniel W. Stashuk. 1286-1291 [doi]
• Multivariate regression model selection with KIC for extrapolation casesAbd-Krim Seghouane. 1292-1295 [doi]
• Robust information clusteringQing Song 0001. 1296-1301 [doi]
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• Sequential relevance vector machine learning from time seriesNikolay I. Nikolaev, Peter Tino. 1308-1313 [doi]
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• Synthesis of binary cellular automata based on binary neural networksTakashi Yamamichi, Toshimichi Saito, Keisuke Taguchi, Hiroyuki Torikai. 1361-1364 [doi]
• Genetic algorithm-based variable translation wavelet neural network and its applicationSteve S. H. Ling, Frank Hung-Fat Leung. 1365-1370 [doi]
• Evolution strategies on connection weights into modified gradient function for multi-layer neural networksSin Chun Ng, Shu Hung Leung, Andrew Luk. 1371-1376 [doi]
• Neural network initialization with prototypes - a case study in function approximationJin-Song Pei, Joseph P. Wright, Andrew W. Smyth. 1377-1382 [doi]
• A novel radial basis function network classifier with centers set by hierarchical clusteringYu-Yen Ou, Yen-Jen Oyang, Chien-Yu Chen 0001. 1383-1388 [doi]
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• The evolving tree, a hierarchical tool for unsupervised data analysisJussi Pakkanen, Jukka livarinen, Erkki Oja. 1395-1399 [doi]
• Optimization of modular neural networks using hierarchical genetic algorithms applied to speech recognitionGabriela E. Martinez, Patricia Melin, Oscar Castillo 0001. 1400-1405 [doi]
• Protein sequence classification using extreme learning machineDianhui Wang, Guang-Bin Huang. 1406-1411 [doi]
• Analyzing the state space property of echo state networks for chaotic system predictionJianhui Xi, Zhiwei Shi, Min Han 0001. 1412-1417 [doi]
• Finding a succinct multi-layer perceptron having shared weightsYusuke Tanahashi, Xiang-Fang Chin, Kazumi Saito, Ryohei Nakano. 1418-1423 [doi]
• Functional organization of the primate prefrontal cortex for memoryMichael Petrides. 1424 [doi]
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• Feed-forward echo state networksMichal Cernanský, Matej Makula. 1479-1482 [doi]
• Direct adaptive control: an echo state network and genetic algorithm approachDongming Xu, Jing Lan, José C. Príncipe. 1483-1486 [doi]
• Nonlinear mappings based on particle swarm optimizationChristian J. Figueroa, Pablo A. Estévez, Rodrigo Hernández. 1487-1492 [doi]
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• Modular network SOM (mnSOM): from vector space to function spaceTetsuo Furukawa, Kazuhiro Tokunaga, Kenji Morishita, Syozo Yasui. 1581-1586 [doi]
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• Nonlinear complex principal component analysis and its applicationsSanjay S. P. Rattan, William W. Hsieh, B. Gerben Ruessink. 1626-1629 [doi]
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• Temporal neural networks for downscaling climate variability and extremesYonas B. Dibike, Paulin Coulibaly. 1636-1641 [doi]
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• Modeling cortico-subcortical interactions during planning, learning, and voluntary control of actionsDaniel Bullock. 1653-1656 [doi]
• Modeling emotional influences on human decision making under riskDaniel S. Levine 0001, Britain Mills, Steven Estrada. 1657-1662 [doi]
• Modelling the interaction of attention and emotionJohn G. Taylor, Nickolaos F. Fragopanagos. 1663-1668 [doi]
• Neural network model for analyzing optic flowKazuya Tohyama, Kunihiko Fukushima. 1669-1674 [doi]
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• Could early visual processes be sufficient to label motions?Pierre Kornprobst, Thierry Vieille, Ivand K. Dimo. 1687-1692 [doi]
• A phase-equation model for a large phase lead in manual tracking caused by intermittent visual informationYasuhiro Takachi, Yasuji Sawada. 1693-1697 [doi]
• Investigations into the analysis of remote sensing images with a growing neural gas pattern recognition algorithmKarl Lalonde. 1698-1703 [doi]
• Applications of neural network methods to the processing of Earth observation satellite dataDiego G. Loyola. 1704-1709 [doi]
• Time dependent neural network models for detecting changes of state in Earth and planetary processesJulio J. Valdés, Graeme Bonham-Carter. 1710-1715 [doi]
• Local and hybrid learning models in forecasting natural phenomenaDimitri P. Solomatine. 1716-1721 [doi]
• Semiblind source separation of climate data detects El Nino as the component with the highest interannual variabilityAlexander Ilin, Harri Valpola, Erkki Oja. 1722-1727 [doi]
• Special session panel discussion: methodological issues in the application of learning methods to climate modeling and Earth sciencesVladimir Cherkassky, Vladimir M. Krasnopolsky, Dimitri P. Solomatine, Julio J. Valdés. 1728 [doi]
• Continuous on-line identification of nonlinear plants in power systems with missing sensor measurementsWei Qiao 0001, Zhi Gao, Ronald G. Harley. 1729-1734 [doi]
• Collective behavior implementation in powerline surveillance sensor networkPornchai Chanyagorn, Harold H. Szu, Hongye Wang. 1735-1739 [doi]
• Neural networks-based scheme for fault diagnosis in fossil electric power plantsJosé Antonio Ruz Hernández, Edgar N. Sánchez, Dionisio A. Suarez. 1740-1745 [doi]
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• Dynamic neural network-based estimator for fault diagnosis in reaction wheel actuator of satellite attitude control systemEhsan Sobhani-Tehrani, Khashayar Khorasani, Siamak Tafazoli. 2347-2352 [doi]
• Modeling of spiral inductors using artificial neural networkTao Liu, Wenjun Zhang 0001, Zhiping Yu. 2353-2358 [doi]
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• Application of neural networks to 3G power amplifier modelingTaijun Liu, Slim Boumaiza, Fadhel M. Ghannouchi. 2378-2382 [doi]
• Bayesian ANN classifier for ECG arrhythmia diagnostic system: a comparison studyDayong Gao, Michael Madden 0002, Des Chambers, Gerard Lyons. 2383-2388 [doi]
• Input and data selection applied to heart disease diagnosisCarlos Eduardo Pedreira, Leonardo Macrini, Elaine S. Costa. 2389-2393 [doi]
• Reliable determination of sleep versus wake from heart rate variability using neural networksAaron Lewicke, Edward S. Sazonov, Michael J. Corwin, Stephanie A. C. Schuckers. 2394-2399 [doi]
• Neural network based detection of fetal heart rate patternsPhilip A. Warrick, Emily F. Hamilton, Maciej Macieszczak. 2400-2405 [doi]
• Exploratory data analysis of dynamic cerebral contrast-enhanced perfusion MRI time-seriesOliver Lange, Anke Meyer-Baese, Axel Wismüller. 2406-2410 [doi]
• A neural network-based prediction model of AR inhibitory activity from a sparse set of compoundsRafael Parra-Hernandez, Erik M. Laxdal, Nikitas J. Dimopoulos, Polyxeni Alexiou. 2411-2416 [doi]
• Multi-class support vector machines for modeling HIV/AIDS treatment adherence using patient dataZhao Lu, Hao Ying 0001, Feng Lin 0001, Stewart Neufeld, Mark Luborsky, David M. Brawn, Andrea Sankar. 2417-2422 [doi]
• Neural network-based approach for the classification of wireless-capsule endoscopic imagesVassilis S. Kodogiannis, Maria Boulougoura. 2423-2428 [doi]
• The use of clustering to analyze symptom-based case definitions for acute gastrointestinal illnessShannon Majowicz, Deborah A. Stacey. 2429-2434 [doi]
• A SVM approach for detection of hemorrhages in background diabetic retinopathyXiaohui Zhang 0001, Opas Chutatape. 2435-2440 [doi]
• Dynamic feature fusion in the self organising tree map-applied to the segmentation of biofilm imagesMatthew J. Kyan, Ling Guan, Steven Liss. 2441-2446 [doi]
• Independent component analysis and high-order statistics for automatic artifact rejectionNadia Mammone, Francesco Carlo Morabito. 2447-2452 [doi]
• A neural supergranph matching architectureStefan Klinger, Jim Austin. 2453-2458 [doi]
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• Computational intelligence techniques for acute leukemia gene expression data classificationVassilis P. Plagianakos, Dimitris K. Tasoulis, Michael N. Vrahatis. 2469-2474 [doi]
• Virtual reality visual data mining with nonlinear discriminant neural networks: application to leukemia and Alzheimer gene expression dataJulio J. Valdés, Alan J. Barton. 2475-2480 [doi]
• SVMs applied to objective aesthetic evaluation conservative breast cancer treatmentJaime S. Cardoso 0001, Joaquim F. Pinto da Costa, Maria João Cardoso. 2481-2486 [doi]
• Classification of breast abnormalities in digital mammograms using image and BI-RADS features in conjunction with neural networkRinku Panchal, Brijesh K. Verma. 2487-2492 [doi]
• Feasibility of multi-layered perceptron network in discriminating breast magnetic resonance imaging lesionsSreenivas Muthyala, Peter Gibbs, Lindsay Turnbull. 2493-2495 [doi]
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• Neural network classification of EEG signals using time-frequency representationChandan Gope, Nasser Kehtarnavaz, Dinish Nair. 2502-2507 [doi]
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• On the use of clustering and local singular spectrum analysis to remove ocular artifacts from electroencephalogramsAna Rita Teixeira, Ana Maria Tomé, Elmar Wolfgang Lang, Peter Gruber 0002, António Martins da Silva. 2514-2519 [doi]
• A fully approach for key variables identification of EMG evaluation signalYanfeng Hou, Jacek M. Zurada, Waldemar Karwowski, William M. Marras. 2520-2525 [doi]
• Extraction of event-related potentials from EEG signals using ICA with referenceLau Kah Liong Joshua, Jagath C. Rajapakse. 2526-2531 [doi]
• PCA and ICA for the extraction of EEG components in cerebral death assessmentFabio La Foresta, Francesco Carlo Morabito, Bruno Azzerboni, Maurizio Ipsale. 2532-2537 [doi]
• Vector quantized radial basis function neural network with embedded multiple local linear models for financial predictionTony Jan, Maria H. Kim. 2538-2543 [doi]
• Application of multi-branch neural networks to stock market predictionTakashi Yamashita, Kotaro Hirasawa, Jinglu Hu. 2544-2548 [doi]
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• A novel image retrieval system based on BP neural networkJun-Hua Han, De-Shuang Huang, Tat-Ming Lok, Michael R. Lyu. 2561-2564 [doi]
• Speaker identification using speech and lip featuresGuobin Ou, Xin Li, XiaoCao Yao, Hongbin Jia, Yi Lu Murphey. 2565-2570 [doi]
• Comparison of a SOM based sequence analysis system and naive Bayesian classifier for spam filteringXiao Luo 0002, Nur Zincir-Heywood. 2571-2576 [doi]
• Neural networks following a binary approach applied to the integer prime-factorization problemBoris Jansen, Kenji Nakayama. 2577-2582 [doi]
• Artificial intelligence for conflict managementE. Habtemariam, Tshilidzi Marwala, Monica Lagazio. 2583-2588 [doi]
• Fingerprint recognition using modular neural networks and fuzzy integrals for response integrationPatricia Melin, Diana Bravo, Oscar Castillo 0001. 2589-2594 [doi]
• Chaotic associative memory and private v-mailsHarold Szu, Ming-Kai Hsu. 2595-2600 [doi]
• Using domain knowledge to constrain structure learning in a Bayesian bioagent detectorAnshu Saksena, Dennis Lucarelli, I-Jeng Wang. 2601-2606 [doi]
• Power system reduced model by artificial neural networksJuan M. Ramirez. 2607-2612 [doi]
• Short term load forecasting for Iran National Power System and its regions using multi layer perceptron and fuzzy inference systemsRoohollah Barzamini, Mohammad Bagher Menhaj, Abbas Khosravi, Shadi Kamalvand. 2619-2624 [doi]
• Artificial neural networks for temporal processing applied to prediction of electric energy in small hydroelectric power stationsPaulo Cesar Endo Joaquim, João Luís Garcia Rosa. 2625-2630 [doi]
• Streamflow forecasting using neural networks and fuzzy clustering techniquesIvette Luna, Secundino Soares, Marina H. Magalhaes, Rosangela Ballini. 2631-2636 [doi]
• Neural network modelling of the 20-year flood event for catchments across the UKChristian W. Dawson, Robert J. Abrahart, Asaad Y. Shamseldin, Robert L. Wilby, Linda M. See. 2637-2642 [doi]
• Modelling harbour sedimentation using ANN and M5 model treesBiswanath Bhattacharya, Dimitri P. Solomatine. 2643-2648 [doi]
• Classification of infrasound events using radial basis function neural networksFredric M. Ham, Kamel Rekab, Sungjin Park, Ranjan Acharyya, Young-Chan Lee. 2649-2654 [doi]
• Neural network river discharge forecasters: an empirical investigation of hidden unit processing functions based on two different catchmentsAsaad Y. Shamseldin, Robert J. Abrahart, Linda M. See. 2655-2660 [doi]
• Robustness of the NN Approach to emulating atmospheric long wave radiation in complex climate modelsVladimir M. Krasnopolsky, Michael S. Fox-Rabinovitz, Ming-Dah Chou. 2661-2665 [doi]
• A comparative study of artificial neural network techniques for river stage forecastingChristian W. Dawson, Linda M. See, Robert J. Abrahart, Robert L. Wilby, Asaad Y. Shamseldin, Francois Anctil, Ahmed Nabil Belbachir, Gavin J. Bowden, Graeme C. Dandy, Nicolas Lauzon, Holger R. Maier. 2666-2670 [doi]
• Issues in designing automated minimal resource allocation neural networksMomcilo Markus. 2671-2673 [doi]
• Wavelet networks: an alternative to classical neural networksKamban Parasuraman, Amin Elshorbagy. 2674-2679 [doi]
• Streamflow forecasting with uncertainty estimate using Bayesian learning for ANNMohammad Sajjad Khan, Paulin Coulibaly. 2680-2685 [doi]
• Intelligent systems for meteorological events forecastEros Pasero, Walter Moniaci, Tassilo Meindl. 2686-2688 [doi]
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• Machine learning in soil classificationBiswanath Bhattacharya, Dimitri P. Solomatine. 2694-2699 [doi]
• Estimation of prediction intervals for the model outputs using machine learningDurga L. Shrestha, Dimitri P. Solomatine. 2700-2705 [doi]
• Determination of the optimal batch size in incremental approaches: an application to tornado detectionHyung-Jin Son, Theodore B. Trafalis, Michael B. Richman. 2706-2710 [doi]
• Locally recurrent neural networks optimal filtering algorithms: application to wind speed prediction using spatial correlationThanasis G. Barbounis, John B. Theocharis. 2711-2716 [doi]
• Neural networks for feedback control of robots and dynamical systemsFrank L. Lewis. 2717 [doi]
• Automatic source attribution of text: a neural networks approachFoaad Khosmood, Franz J. Kurfess. 2718-2723 [doi]
• Cross-entropy approach to data visualization based on the neural gas networkPablo A. Estévez, Cristián J. Figueroa, Kazumi Saito. 2724-2729 [doi]
• A model of document clustering using ant colony algorithm and validity indexYan Yang 0001, Mohamed Kamel, Fan Jin. 2730-2735 [doi]
• A method to extract non-linear principal components of large datasets - an application in skill transferSilvia S. C. Botelho, Rodrigo de Bern, Matheus Bacelo de Figueiredo, Willian Lautenschlger, Tania Mezzadri Centeno, Mauricio M. Mata. 2736-2741 [doi]
• Identifying knowledge domain and incremental new class learning in SVMHongbin Jia, Yi Lu Murphey, Daniel Gutchess, Tzyy-Shuh Chang. 2742-2747 [doi]
• Real-world text clustering with adaptive resonance theory neural networksLouis Massey. 2748-2753 [doi]
• Hebbian synaptic modification in cortical circuits and memory-guided behavior in spatial alternation and delayed non-match to positionMichael E. Hasselmo, Eric A. Zilli. 2754-2759 [doi]
• Faithful retinotopic maps with local optimum rules, axonal competition, and Hebbian learningJean-Philippe Thivierge, Evan Balaban. 2760-2765 [doi]
• Modeling structural plasticity in the barn owl auditory localization system with a spike-time dependent Hebbian learning ruleShreesh P. Mysore, Steven R. Quartz. 2766-2771 [doi]
• Hebbian motor control in a robot-embedded model of habituationSylvain Sirois. 2772-2777 [doi]
• Constraints on body movement during visual development affect behavior of evolutionary robotsMototaka Suzuki, Dario Floreano, Ezequiel A. Di Paolo. 2778-2783 [doi]
• Dual Kalman filters for autonomous terrain aided navigation in unknown environmentsAnindya Sao Paul, Eric A. Wan. 2784-2789 [doi]
• New memory model for humanoid robots - introduction of co-associative memory using mutually coupled chaotic neural networksKazuko Itoh, Hiroyasu Miwa, Yuko Nukariya, Massimiliano Zecca, Hideaki Takanobu, Paolo Dario, Atsuo Takanishi. 2790-2795 [doi]
• Bi-criteria torque optimization of redundant manipulators based on a simplified dual neural networkShubao Liu, Jun Wang 0002. 2796-2801 [doi]
• Spatio-temporal neural data mining architecture in learning robotsJames Malone, Mark Elshaw, Ken McGarry, Chris Bowerman, Stefan Wermter. 2802-2807 [doi]
• Fault-tolerance by regeneration: using development to achieve robust self-healing neural networksDiego Federici. 2808-2813 [doi]
• Maximal variation and missing values for componentwise support vector machinesKristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor, Jos De Brabanter. 2814-2819 [doi]
• Fast Bayesian support vector machine parameter tuning with the Nystrom methodCarl Gold, Peter Sollich. 2820-2825 [doi]
• Maximum margin classifiers with noisy data: a robust optimization approachTheodore B. Trafalis, Robin C. Gilbert. 2826-2830 [doi]
• Data classification with a relaxed model of variable kernel density estimationYen-Jen Oyang, Yu-Yen Ou, Shien-Ching Hwang, Chien-Yu Chen 0001, Darby Tien-Hao Chang. 2831-2836 [doi]
• A ridgelet kernel approach for regression using particle swarm optimization algorithmShuyuan Yang, Min Wang 0007, Licheng Jiao. 2837-2842 [doi]
• Ultra low power fault tolerant neural inspired CMOS logicSnorre Aunet, Valeriu Beiu. 2843-2848 [doi]
• Noise performance of single-electron depressing synapses for neuronal synchrony detectionTakahide Oya, Tetsuya Asai, Ryo Kagaya, Yoshihito Amemiya. 2849-2854 [doi]
• A new pulse mode self organizing map hardware with digital phase locked loopsHiroomi Hikawa. 2855-2860 [doi]
• A novel approach to reduce interconnect complexity in ANN hardware implementationLuiz Brunelli, Elmar U. K. Melcher, Alisson Vasconcelos De Brito, Raimundo C. S. Freire. 2861-2866 [doi]
• A reconfigurable parallel architecture for SVM classificationIan Biasi, Andrea Boni, Alessandro Zorat. 2867-2872 [doi]
• An integrate and fire model of prefrontal cortex provides a biological implementation of action selection in reinforcement learning theory that reuses known representationsRandal A. Koene, Michael E. Hasselmo. 2873-2878 [doi]
• Knowing your place: subfield specific involvement in hippocampal spatial processingMatthew Hartley, Neill R. Taylor, John G. Taylor. 2879-2884 [doi]
• Coherent learning in cortical maps: a generic approachOlivier Ménard, Hervé Frezza-Buet. 2885-2890 [doi]
• Using neural dynamics to switch attentionJulien Vitay, Nicolas P. Rougier. 2891-2896 [doi]
• A continuous attractor neural network model of divided visual attentionDominic I. Standage, Thomas Trappenberg, Raymond M. Klein. 2897-2902 [doi]
• Bridging the gap between vision and language: a morphodynamical model of spatial categoriesRené Doursat, Jean Petitot. 2903-2908 [doi]
• Challenges in verification and validation of autonomous systems for space explorationGuillaume Brat, Ari K. Jónsson. 2909-2914 [doi]
• Rule extraction as a formal method for the verification and validation of neural networksBrian J. Taylor, Marjorie A. Darrah 0001. 2915-2920 [doi]
• An approach to predicting non-deterministic neural network behaviorEdgar Fuller, Sampath Yerramalla, Bojan Cukic, Srikanth Gururajan. 2921-2926 [doi]
• Testing decision systems with classification componentsDragos D. Margineantu, Michael Drumheller, Roman D. Fresnedo. 2927-2930 [doi]
• Validity index in dynamic cell structuresYan Liu 0003, Bojan Cukic, Sampath Yerramalla, Srikanth Gururajan. 2931-2936 [doi]
• A disturbance rejection based neural network algorithm for control of air pollution emissionsSteve Piche, Paul Sabiston. 2937-2941 [doi]
• A gradient descending solution to the LASSO criteriaNan Zhang 0002, Shuqing Zeng. 2942-2947 [doi]
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• Reinforcement learning and the frame problemRoberto A. Santiago, George G. Lendaris. 2971-2976 [doi]
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