Table of Contents

• Evidence for a Forward Dynamics Model in Human Adaptive Motor ControlNikhil Bhushan, Reza Shadmehr. 3-9 [doi]
• Perceiving without Learning: From Spirals to Inside/Outside RelationsKe Chen, DeLiang L. Wang. 10-16 [doi]
• A Model for Associative MultiplicationG. Bjorn Christianson, Suzanna Becker. 17-23 [doi]
• Facial Memory Is Kernel Density Estimation (Almost)Matthew N. Dailey, Garrison W. Cottrell, Thomas A. Busey. 24-30 [doi]
• Multiple Paired Forward-Inverse Models for Human Motor Learning and ControlMasahiko Haruno, Daniel M. Wolpert, Mitsuo Kawato. 31-37 [doi]
• Utilizing lime: Asynchronous BindingBradley C. Love. 38-44 [doi]
• Mechanisms of Generalization in Perceptual LearningZili Liu, Daphna Weinshall. 45-51 [doi]
• A Principle for Unsupervised Hierarchical Decomposition of Visual ScenesMichael Mozer. 52-58 [doi]
• Bayesian Modeling of Human Concept LearningJoshua B. Tenenbaum. 59-68 [doi]
• Temporally Asymmetric Hebbian Learning, Spike liming and Neural Response VariabilityL. F. Abbott, Sen Song. 69-75 [doi]
• Contrast Adaptation in Simple Cells by Changing the Transmitter Release ProbabilityPéter Adorján, Klaus Obermayer. 76-82 [doi]
• Where Does the Population Vector of Motor Cortical Cells Point during Reaching Movements?Pierre Baraduc, Emmanuel Guigon, Yves Burnod. 83-89 [doi]
• Recurrent Cortical Amplification Produces Complex Cell ResponsesFrances S. Chance, Sacha B. Nelson, L. F. Abbott. 90-96 [doi]
• Neuronal Regulation Implements Efficient Synaptic PruningGal Chechik, Isaac Meilijson, Eytan Ruppin. 97-103 [doi]
• Divisive Normalization, Line Attractor Networks and Ideal ObserversSophie Deneve, Alexandre Pouget, Peter E. Latham. 104-110 [doi]
• Synergy and Redundancy among Brain Cells of Behaving MonkeysItay Gat, Naftali Tishby. 111-117 [doi]
• Analyzing and Visualizing Single-Trial Event-Related PotentialsTzyy-Ping Jung, Scott Makeig, Marissa Westerfield, Jeanne Townsend, Eric Courchesne, Terrence J. Sejnowski. 118-124 [doi]
• Spike-Based Compared to Rate-Based Hebbian LearningRichard Kempter, Wulfram Gerstner, J. Leo van Hemmen. 125-131 [doi]
• Signal Detection in Noisy Weakly-Active DendritesAmit Manwani, Christof Koch. 132-138 [doi]
• The Role of Lateral Cortical Competition in Ocular Dominance DevelopmentChristian Piepenbrock, Klaus Obermayer. 139-145 [doi]
• Multi-Electrode Spike Sorting by Clustering Transfer FunctionsDmitry Rinberg, Hanan Davidowitz, Naftali Tishby. 146-152 [doi]
• Modeling Surround Suppression in V1 Neurons with a Statistically Derived Normalization ModelEero P. Simoncelli, Odelia Schwartz. 153-159 [doi]
• Information Maximization in Single NeuronsMartin Stemmler, Christof Koch. 160-166 [doi]
• The Effect of Correlations on the Fisher Information of Population CodesHyoungsoo Yoon, Haim Sompolinsky. 167-173 [doi]
• Distributional Population Codes and Multiple Motion ModelsRichard S. Zemel, Peter Dayan. 174-182 [doi]
• Tractable Variational Structures for Approximating Graphical ModelsDavid Barber, Wim Wiegerinck. 183-189 [doi]
• Almost Linear VC Dimension Bounds for Piecewise Polynomial NetworksPeter L. Bartlett, Vitaly Maiorov, Ron Meir. 190-196 [doi]
• Dynamics of Supervised Learning with Restricted Training SetsAnthony C. C. Coolen, David Saad. 197-203 [doi]
• Dynamically Adapting Kernels in Support Vector MachinesNello Cristianini, Colin Campbell, John Shawe-Taylor. 204-210 [doi]
• Phase Diagram and Storage Capacity of Sequence-Storing Neural NetworksA. Düring, Anthony C. C. Coolen, D. Sherrington. 211-217 [doi]
• Finite-Dimensional Approximation of Gaussian ProcessesGiancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper. 218-224 [doi]
• Linear Hinge Loss and Average MarginClaudio Gentile, Manfred K. Warmuth. 225-231 [doi]
• Unsupervised and Supervised Clustering: The Mutual Information between Parameters and ObservationsDidier Herschkowitz, Jean-Pierre Nadal. 232-238 [doi]
• Convergence of the Wake-Sleep AlgorithmShiro Ikeda, Shun-ichi Amari, Hiroyuki Nakahara. 239-245 [doi]
• The Belief in TAPYoshiyuki Kabashima, David Saad. 246-252 [doi]
• Optimizing Classifers for Imbalanced Training SetsGrigoris J. Karakoulas, John Shawe-Taylor. 253-259 [doi]
• Inference in Multilayer Networks via Large Deviation BoundsMichael J. Kearns, Lawrence K. Saul. 260-266 [doi]
• Stationarity and Stability of Autoregressive Neural Network ProcessesFriedrich Leisch, Adrian Trapletti, Kurt Hornik. 267-273 [doi]
• Computational Differences between Asymmetrical and Symmetrical NetworksZhaoping Li, Peter Dayan. 274-280 [doi]
• A Precise Characterization of the Class of Languages Recognized by Neural Nets under Gaussian and Other Common Noise DistributionsWolfgang Maass, Eduardo D. Sontag. 281-287 [doi]
• Direct Optimization of Margins Improves Generalization in Combined ClassifiersLlew Mason, Peter L. Bartlett, Jonathan Baxter. 288-294 [doi]
• On the Optimality of Incremental Neural Network AlgorithmsRon Meir, Vitaly Maiorov. 295-301 [doi]
• General Bounds on Bayes Errors for Regression with Gaussian ProcessesManfred Opper, Francesco Vivarelli. 302-308 [doi]
• Mean Field Methods for Classification with Gaussian ProcessesManfred Opper, Ole Winther. 309-315 [doi]
• On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General TheoriesH. C. Rae, Peter Sollich, Anthony C. C. Coolen. 316-322 [doi]
• Tight Bounds for the VC-Dimension of Piecewise Polynomial NetworksAkito Sakurai. 323-329 [doi]
• Shrinking the Tube: A New Support Vector Regression AlgorithmBernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson. 330-336 [doi]
• Discontinuous Recall Transitions Induced by Competition Between Short- and Long-Range Interactions in Recurrent NetworksN. S. Skantzos, C. F. Beckmann, Anthony C. C. Coolen. 337-345 [doi]
• Learning Curves for Gaussian ProcessesPeter Sollich. 344-350 [doi]
• A Theory of Mean Field ApproximationToshiyuki Tanaka. 351-360 [doi]
• Learning a Hierarchical Belief Network of Independent Factor AnalyzersHagai Attias. 361-367 [doi]
• Semi-Supervised Support Vector MachinesKristin P. Bennett, Ayhan Demiriz. 368-374 [doi]
• Lazy Learning Meets the Recursive Least Squares AlgorithmMauro Birattari, Gianluca Bontempi, Hugues Bersini. 375-381 [doi]
• Bayesian PCAChristopher M. Bishop. 382-388 [doi]
• Learning Multi-Class DynamicsAndrew Blake, Ben North, Michael Isard. 389-395 [doi]
• Approximate Learning of Dynamic ModelsXavier Boyen, Daphne Koller. 396-402 [doi]
• Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space ModelsThomas Briegel, Volker Tresp. 403-409 [doi]
• Global Optimisation of Neural Network Models via Sequential SamplingJoão F. G. de Freitas, Mahesan Niranjan, Arnaud Doucet, Andrew H. Gee. 410-416 [doi]
• Efficient Bayesian Parameter Estimation in Large Discrete DomainsNir Friedman, Yoram Singer. 417-423 [doi]
• A Randomized Algorithm for Pairwise ClusteringYoram Gdalyahu, Daphna Weinshall, Michael Werman. 424-430 [doi]
• Learning Nonlinear Dynamical Systems Using an EM AlgorithmZoubin Ghahramani, Sam T. Roweis. 431-437 [doi]
• Classification on Pairwise Proximity DataThore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, Klaus Obermayer. 438-444 [doi]
• Outcomes of the Equivalence of Adaptive Ridge with Least Absolute ShrinkageYves Grandvalet, Stéphane Canu. 445-451 [doi]
• Visualizing Group StructureMarcus Held, Jan Puzicha, Joachim M. Buhmann. 452-458 [doi]
• Source Separation as a By-Product of RegularizationSepp Hochreiter, Jürgen Schmidhuber. 459-465 [doi]
• Learning from Dyadic DataThomas Hofmann, Jan Puzicha, Michael I. Jordan. 466-472 [doi]
• Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood EstimationAapo Hyvärinen, Patrik O. Hoyer, Erkki Oja. 473-479 [doi]
• Restructuring Sparse High Dimensional Data for Effective RetrievalCharles Lee Isbell Jr., Paul A. Viola. 480-486 [doi]
• Exploiting Generative Models in Discriminative ClassifiersTommi Jaakkola, David Haussler. 487-493 [doi]
• Maximum Conditional Likelihood via Bound Maximization and the CEM AlgorithmTony Jebara, Alex Pentland. 494-500 [doi]
• A Polygonal Line Algorithm for Constructing Principal CurvesBalázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger. 501-507 [doi]
• Unsupervised Classification with Non-Gaussian Mixture Models Using ICATe-Won Lee, Michael S. Lewicki, Terrence J. Sejnowski. 508-514 [doi]
• Learning a Continuous Hidden Variable Model for Binary DataDaniel D. Lee, Haim Sompolinsky. 515-521 [doi]
• Neural Networks for Density EstimationMalik Magdon-Ismail, Amir F. Atiya. 522-528 [doi]
• Exploratory Data Analysis Using Radial Basis Function Latent Variable ModelsAlan D. Marrs, Andrew R. Webb. 529-535 [doi]
• Kernel PCA and De-Noising in Feature SpacesSebastian Mika, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch. 536-542 [doi]
• Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-TreesAndrew W. Moore. 543-549 [doi]
• Replicator Equations, Maximal Cliques, and Graph IsomorphismMarcello Pelillo. 550-556 [doi]
• Using Analytic QP and Sparseness to Speed Training of Support Vector MachinesJohn C. Platt. 557-563 [doi]
• Regularizing AdaBoostGunnar Rätsch, Takashi Onoda, Klaus-Robert Müller. 564-570 [doi]
• Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural NetworksPatrice Simard, Léon Bottou, Patrick Haffner, Yann LeCun. 571-577 [doi]
• Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint EntropyYoram Singer, Manfred K. Warmuth. 578-584 [doi]
• Semiparametric Support Vector and Linear Programming MachinesAlex J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf. 585-591 [doi]
• Probabilistic Visualisation of High-Dimensional Binary DataMichael E. Tipping. 592-598 [doi]
• SMEM Algorithm for Mixture ModelsNaonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton. 599-605 [doi]
• Learning Mixture HierarchiesNuno Vasconcelos, Andrew Lippman. 606-612 [doi]
• Discovering Hidden Features with Gaussian Processes RegressionFrancesco Vivarelli, Christopher K. I. Williams. 613-619 [doi]
• The Bias-Variance Tradeoff and the Randomized GACVGrace Wahba, Xiwu Lin, Fangyu Gao, Dong Xiang, Ronald Klein, Barbara Klein. 620-626 [doi]
• Basis Selection for Wavelet RegressionKevin R. Wheeler, Atam P. Dhawan. 627-633 [doi]
• DTs: Dynamic TreesChristopher K. I. Williams, Nicholas J. Adams. 634-640 [doi]
• Convergence Rates of Algorithms for Visual Search: Detecting Visual ContoursAlan L. Yuille, James M. Coughlan. 641-647 [doi]
• Blind Separation of Filtered Sources Using State-Space ApproachLiqing Zhang, Andrzej Cichocki. 648-656 [doi]
• Analog VLSI Cellular Implementation of the Boundary Contour SystemGert Cauwenberghs, James Waskiewicz. 657-663 [doi]
• Active Noise Canceling Using Analog Neuro-Chip with On-Chip Learning CapabilityJung-Wook Cho, Soo-Young Lee. 664-670 [doi]
• A Micropower CMOS Adaptive Amplitude and Shift Invariant Vector QuantiserRichard Coggins, Raymond J. Wang, Marwan A. Jabri. 671-677 [doi]
• Optimizing Correlation Algorithms for Hardware-Based Transient ClassificationR. Timothy Edwards, Gert Cauwenberghs, Fernando J. Pineda. 678-684 [doi]
• VLSI Implementation of Motion Centroid Localization for Autonomous NavigationRalph Etienne-Cummings, Viktor Gruev, Mohammed Abdel Ghani. 685-691 [doi]
• A Neuromorphic Monaural Sound LocalizerJohn G. Harris, Chiang-Jung Pu, José Carlos Príncipe. 692-698 [doi]
• An Integrated Vision Sensor for the Computation of Optical Flow Singular PointsCharles M. Higgins, Christof Koch. 699-705 [doi]
• Computation of Smooth Optical Flow in a Feedback Connected Analog NetworkAlan Stocker, Rodney J. Douglas. 706-712 [doi]
• A High Performance k-NN Classifier Using a Binary Correlation Matrix MemoryPing Zhou, Jim Austin, John Kennedy. 713-722 [doi]
• An Entropic Estimator for Structure DiscoveryMatthew Brand. 723-729 [doi]
• Coding Time-Varying Signals Using Sparse, Shift-Invariant RepresentationsMichael S. Lewicki, Terrence J. Sejnowski. 730-736 [doi]
• Controlling the Complexity of HMM Systems by RegularizationChristoph Neukirchen, Gerhard Rigoll. 737-743 [doi]
• Maximum-Likelihood Continuity Mapping (MALCOM): An Alternative to HMMsDavid A. Nix, John E. Hogden. 744-750 [doi]
• Markov Processes on Curves for Automatic Speech RecognitionLawrence K. Saul, Mazin G. Rahim. 751-760 [doi]
• A Phase Space Approach to Minimax Entropy Learning and the Minutemax ApproximationsJames M. Coughlan, Alan L. Yuille. 761-767 [doi]
• Example-Based Image Synthesis of Articulated FiguresTrevor Darrell. 768-774 [doi]
• Learning to Estimate Scenes from ImagesWilliam T. Freeman, Egon C. Pasztor. 775-781 [doi]
• Learning to Find Pictures of PeopleSergey Ioffe, David A. Forsyth. 782-788 [doi]
• Attentional Modulation of Human Pattern Discrimination Psychophysics Reproduced by a Quantitative ModelLaurent Itti, Jochen Braun, Dale K. Lee, Christof Koch. 789-795 [doi]
• A V1 Model of Pop Out and Asymmetty in Visual SearchZhaoping Li. 796-802 [doi]
• Support Vector Machines Applied to Face RecognitionP. Jonathon Phillips. 803-809 [doi]
• Learning Lie Groups for Invariant Visual PerceptionRajesh P. N. Rao, Daniel L. Ruderman. 810-816 [doi]
• General-Purpose Localization of Textured Image RegionsRuth Rosenholtz. 817-823 [doi]
• Probabilistic Image Sensor FusionRavi K. Sharma, Todd K. Leen, Misha Pavel. 824-830 [doi]
• Orientation, Scale, and Discontinuity as Emergent Properties of Illusory Contour ShapeKarvel K. Thornber, Lance R. Williams. 831-837 [doi]
• Classification in Non-Metric SpacesDaphna Weinshall, David W. Jacobs, Yoram Gdalyahu. 838-846 [doi]
• Making Templates Rotationally Invariant. An Application to Rotated Digit RecognitionShumeet Baluja. 847-853 [doi]
• Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled DataShumeet Baluja. 854-860 [doi]
• Adding Constrained Discontinuities to Gaussian Process Models of Wind FieldsDan Cornford, Ian T. Nabney, Christopher K. I. Williams. 861-867 [doi]
• Vertex Identification in High Energy Physics ExperimentsGideon Dror, Halina Abramowicz, David Horn. 868-874 [doi]
• Familiarity Discrimination of Radar PulsesEric Granger, Stephen Grossberg, Mark A. Rubin, William W. Streilein. 875-881 [doi]
• Fast Neural Network Emulation of Dynamical Systems for Computer AnimationRadek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton. 882-888 [doi]
• Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching ModelJaakko Hollmén, Volker Tresp. 889-895 [doi]
• Graph Matching for Shape RetrievalBenoit Huet, Andrew D. J. Cross, Edwin R. Hancock. 896-902 [doi]
• Scheduling Straight-Line Code Using Reinforcement Learning and RolloutsAmy McGovern, J. Eliot B. Moss. 903-909 [doi]
• Bayesian Modeling of Facial SimilarityBaback Moghaddam, Tony Jebara, Alex Pentland. 910-916 [doi]
• Reinforcement Learning for TradingJohn E. Moody, Matthew Saffell. 917-923 [doi]
• Graphical Models for Recognizing Human InteractionsNuria Oliver, Barbara Rosario, Alex Pentland. 924-930 [doi]
• Independent Component Analysis of Intracellular Calcium Spike DataKlaus Prank, Julia Börger, Alexander von zur Mühlen, Georg Brabant, Christof Schöfl. 931-937 [doi]
• Applications of Multi-Resolution Neural Networks to MammographyClay Spence, Paul Sajda. 938-944 [doi]
• Robot Docking Using Mixtures of GaussiansMatthew M. Williamson, Roderick Murray-Smith, Volker Hansen. 945-951 [doi]
• Using Collective Intelligence to Route Internet TrafficDavid Wolpert, Kagan Tumer, Jeremy Frank. 952-960 [doi]
• Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game AlgorithmMohammad A. Al-Ansari, Ronald J. Williams. 961-967 [doi]
• Gradient Descent for General Reinforcement LearningLeemon C. Baird III, Andrew W. Moore. 968-974 [doi]
• Non-Linear PI Control Inspired by Biological Control SystemsLyndon J. Brown, Gregory E. Gonye, James S. Schwaber. 975-981 [doi]
• Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement LearningTimothy X. Brown, Hui Tong, Satinder P. Singh. 982-988 [doi]
• Viewing Classifier Systems as Model Free Learning in POMDPsAkira Hayashi, Nobuo Suematsu. 989-995 [doi]
• Finite-Sample Convergence Rates for Q-Learning and Indirect AlgorithmsMichael J. Kearns, Satinder P. Singh. 996-1002 [doi]
• Exploring Unknown Environments with Real-Time Search or Reinforcement LearningSven Koenig. 1003-1009 [doi]
• The Effect of Eligibility Traces on Finding Optimal Memoryless Policies in Partially Observable Markov Decision ProcessesJohn Loch. 1010-1016 [doi]
• Learning Instance-Independent Value Functions to Enhance Local SearchRobert Moll, Andrew G. Barto, Theodore J. Perkins, Richard S. Sutton. 1017-1023 [doi]
• Barycentric Interpolators for Continuous Space and Time Reinforcement LearningRémi Munos, Andrew W. Moore. 1024-1030 [doi]
• Risk Sensitive Reinforcement LearningRalph Neuneier, Oliver Mihatsch. 1031-1037 [doi]
• Coordinate Transformation Learning of Hand Position Feedback Controller by Using Change of Position Error NormEimei Oyama, Susumu Tachi. 1038-1044 [doi]
• Learning Macro-Actions in Reinforcement LearningJette Randlov. 1045-1051 [doi]
• Reinforcement Learning Based on On-Line EM AlgorithmMasa-aki Sato, Shin Ishii. 1052-1058 [doi]
• A Reinforcement Learning Algorithm in Partially Observable Environments Using Short-Term MemoryNobuo Suematsu, Akira Hayashi. 1059-1065 [doi]
• Improved Switching among Temporally Abstract ActionsRichard S. Sutton, Satinder P. Singh, Doina Precup, Balaraman Ravindran. 1066-1072 [doi]
• Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision ProcessesJohn K. Williams, Satinder P. Singh. 1073-1080 [doi]