Table of Contents

• Modeling Temporal Structure in Classical ConditioningAaron C. Courville, David S. Touretzky. 3-10 [doi]
• Motivated Reinforcement LearningPeter Dayan. 11-18 [doi]
• Probabilistic principles in unsupervised learning of visual structure: human data and a modelShimon Edelman, Benjamin P. Hiles, Hwajin Yang, Nathan Intrator. 19-26 [doi]
• Fragment Completion in Humans and MachinesDavid Jacobs, Bas Rokers, Archisman Rudra, Zili Liu. 27-34 [doi]
• Natural Language Grammar Induction Using a Constituent-Context ModelDan Klein, Christopher D. Manning. 35-42 [doi]
• The Emergence of Multiple Movement Units in the Presence of Noise and Feedback DelayMichael Kositsky, Andrew G. Barto. 43-50 [doi]
• A Rational Analysis of Cognitive Control in a Speeded Discrimination TaskMichael C. Mozer, Michael D. Colagrosso, David E. Huber. 51-57 [doi]
• A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence ProcessingS. Narayanan, Daniel Jurafsky. 59-65 [doi]
• Grammar Transfer in a Second Order Recurrent Neural NetworkMichiro Negishi, Stephen Jose Hanson. 67-73 [doi]
• Generalizable Relational Binding from Coarse-coded Distributed RepresentationsRandall C. O Reilly, R. S. Busby. 75-82 [doi]
• A Model of the Phonological Loop: Generalization and BindingRandall C. O Reilly, R. Soto. 83-90 [doi]
• Grammatical BigramsMark A. Paskin. 91-97 [doi]
• Causal Categorization with Bayes NetsBob Rehder. 99-105 [doi]
• Constructing Distributed Representations Using Additive ClusteringW. Ruml. 107-114 [doi]
• Reinforcement Learning and Time Perception -- a Model of Animal ExperimentsJonathan L. Shapiro, J. Wearden. 115-122 [doi]
• A Quantitative Model of Counterfactual ReasoningDaniel Yarlett, Michael Ramscar. 123-130 [doi]
• Bayesian morphometry of hippocampal cells suggests same-cell somatodendritic repulsionGiorgio A. Ascoli, Alexei V. Samsonovich. 133-139 [doi]
• Modularity in the motor system: decomposition of muscle patterns as combinations of time-varying synergiesA. d Avella, M. C. Tresch. 141-148 [doi]
• Receptive field structure of flow detectors for heading perceptionJ. A. Beintema, A. V. van den Berg, M. Lappe. 149-156 [doi]
• Classifying Single Trial EEG: Towards Brain Computer InterfacingBenjamin Blankertz, Gabriel Curio, Klaus-Robert Müller. 157-164 [doi]
• Orientational and Geometric Determinants of Place and Head-directionNeil Burgess, Tom Hartley. 165-172 [doi]
• Group Redundancy Measures Reveal Redundancy Reduction in the Auditory PathwayGal Chechik, Amir Globerson, M. J. Anderson, E. D. Young, Israel Nelken, Naftali Tishby. 173-180 [doi]
• A Maximum-Likelihood Approach to Modeling Multisensory EnhancementH. Colonius, A. Diederich. 181-187 [doi]
• ACh, Uncertainty, and Cortical InferencePeter Dayan, Angela J. Yu. 189-196 [doi]
• Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force FieldO. Donchin, Reza Shadmehr. 197-203 [doi]
• Exact differential equation population dynamics for integrate-and-fire neuronsJulian Eggert, Berthold Bäuml. 205-212 [doi]
• Probabilistic Inference of Hand Motion from Neural Activity in Motor CortexYun Gao, Michael J. Black, Elie Bienenstock, Shy Shoham, John P. Donoghue. 213-220 [doi]
• A theory of neural integration in the head-direction systemRichard H. R. Hahnloser, Xiaohui Xie, H. Sebastian Seung. 221-228 [doi]
• 3 state neurons for contextual processingÁdám Kepecs, S. Raghavachari. 229-236 [doi]
• Associative memory in realistic neuronal networksPeter E. Latham. 237-244 [doi]
• Self-regulation Mechanism of Temporally Asymmetric Hebbian PlasticityN. Matsumoto, M. Okada. 245-252 [doi]
• Information-Geometric Decomposition in Spike AnalysisHiroyuki Nakahara, Shun-ichi Amari. 253-260 [doi]
• Eye movements and the maturation of cortical orientation selectivityAntonino Casile, Michele Rucci. 261-267 [doi]
• Characterizing Neural Gain Control using Spike-triggered CovarianceOdelia Schwartz, E. J. Chichilnisky, Eero P. Simoncelli. 269-276 [doi]
• Correlation Codes in Neuronal PopulationsMaoz Shamir, Haim Sompolinsky. 277-284 [doi]
• Why Neuronal Dynamics Should Control Synaptic Learning RulesJesper Tegnér, Ádám Kepecs. 285-292 [doi]
• Effective Size of Receptive Fields of Inferior Temporal Visual Cortex Neurons in Natural ScenesThomas P. Trappenberg, Edmund T. Rolls, Simon M. Stringer. 293-300 [doi]
• Activity Driven Adaptive Stochastic ResonanceGregor Wenning, Klaus Obermayer. 301-308 [doi]
• Spike timing and the coding of naturalistic sounds in a central auditory area of songbirdsB. D. Wright, Kamal Sen, William Bialek, A. J. Doupe. 309-316 [doi]
• Neural Implementation of Bayesian Inference in Population CodesSi Wu, Shun-ichi Amari. 317-323 [doi]
• Generating velocity tuning by asymmetric recurrent connectionsXiaohui Xie, Martin A. Giese. 325-332 [doi]
• Sampling Techniques for Kernel MethodsDimitris Achlioptas, Frank McSherry, Bernhard Schölkopf. 335-342 [doi]
• Geometrical Singularities in the Neuromanifold of Multilayer PerceptronsShun-ichi Amari, Hyeyoung Park, Tomoko Ozeki. 343-350 [doi]
• The Noisy Euclidean Traveling Salesman Problem and LearningMikio L. Braun, Joachim M. Buhmann. 351-358 [doi]
• On the Generalization Ability of On-Line Learning AlgorithmsNicolò Cesa-Bianchi, Alex Conconi, Claudio Gentile. 359-366 [doi]
• On Kernel-Target AlignmentNello Cristianini, John Shawe-Taylor, André Elisseeff, Jaz S. Kandola. 367-373 [doi]
• PAC Generalization Bounds for Co-trainingSanjoy Dasgupta, Michael L. Littman, David A. McAllester. 375-382 [doi]
• Analysis of Sparse Bayesian LearningAnita C. Faul, Michael E. Tipping. 383-389 [doi]
• Algorithmic LuckinessRalf Herbrich, Robert C. Williamson. 391-397 [doi]
• Distribution of Mutual InformationM. Hutter. 399-406 [doi]
• Information Geometrical Framework for Analyzing Belief Propagation DecoderShiro Ikeda, Toshiyuki Tanaka, Shun-ichi Amari. 407-414 [doi]
• Novel iteration schemes for the Cluster Variation MethodHilbert J. Kappen, Wim Wiegerinck. 415-422 [doi]
• Efficiency versus Convergence of Boolean Kernels for On-Line Learning AlgorithmsRoni Khardon, Dan Roth, Rocco A. Servedio. 423-430 [doi]
• Small-World Phenomena and the Dynamics of InformationJon M. Kleinberg. 431-438 [doi]
• Kernel Machines and Boolean FunctionsAdam Kowalczyk, Alex J. Smola, Robert C. Williamson. 439-446 [doi]
• Boosting and Maximum Likelihood for Exponential ModelsGuy Lebanon, John D. Lafferty. 447-454 [doi]
• Means, Correlations and BoundsM. Leisink, B. Kappen. 455-462 [doi]
• A Variational Approach to Learning CurvesDörthe Malzahn, Manfred Opper. 463-469 [doi]
• Entropy and Inference, RevisitedIlya Nemenman, F. Shafee, William Bialek. 471-478 [doi]
• Asymptotic Universality for Learning Curves of Support Vector MachinesManfred Opper, Robert Urbanczik. 479-486 [doi]
• On the Convergence of LeveragingGunnar Rätsch, Sebastian Mika, Manfred K. Warmuth. 487-494 [doi]
• Scaling Laws and Local Minima in Hebbian ICAMagnus Rattray, Gleb Basalyga. 495-501 [doi]
• Computing Time Lower Bounds for Recurrent Sigmoidal Neural NetworksM. Schmitt. 503-510 [doi]
• On the Concentration of Spectral PropertiesJohn Shawe-Taylor, Nello Cristianini, Jaz S. Kandola. 511-517 [doi]
• Gaussian Process Regression with Mismatched ModelsPeter Sollich. 519-526 [doi]
• Information-Geometrical Significance of Sparsity in Gallager CodesToshiyuki Tanaka, Shiro Ikeda, Shun-ichi Amari. 527-534 [doi]
• Fast Parameter Estimation Using Green s FunctionsK. Y. M. Wong, F. Li. 535-542 [doi]
• Generalization Performance of Some Learning Problems in Hilbert Functional SpacesT. Zhang. 543-550 [doi]
• Semi-supervised MarginBoostFlorence d Alché-Buc, Yves Grandvalet, Christophe Ambroise. 553-560 [doi]
• Rao-Blackwellised Particle Filtering via Data AugmentationChristophe Andrieu, Nando de Freitas, Arnaud Doucet. 561-567 [doi]
• Thin Junction TreesFrancis R. Bach, Michael I. Jordan. 569-576 [doi]
• The Infinite Hidden Markov ModelMatthew J. Beal, Zoubin Ghahramani, Carl Edward Rasmussen. 577-584 [doi]
• Laplacian Eigenmaps and Spectral Techniques for Embedding and ClusteringMikhail Belkin, Partha Niyogi. 585-591 [doi]
• Duality, Geometry, and Support Vector RegressionJ. Bi, K. P. Bennett. 593-600 [doi]
• Latent Dirichlet AllocationDavid M. Blei, Andrew Y. Ng, Michael I. Jordan. 601-608 [doi]
• Incorporating Invariances in Non-Linear Support Vector MachinesOlivier Chapelle, Bernhard Schölkopf. 609-616 [doi]
• A Generalization of Principal Components Analysis to the Exponential FamilyMichael Collins, S. DasGupta, Robert E. Schapire. 617-624 [doi]
• Convolution Kernels for Natural LanguageMichael Collins, Nigel Duffy. 625-632 [doi]
• A Parallel Mixture of SVMs for Very Large Scale ProblemsRonan Collobert, Samy Bengio, Yoshua Bengio. 633-640 [doi]
• Pranking with RankingKoby Crammer, Yoram Singer. 641-647 [doi]
• Spectral Kernel Methods for ClusteringNello Cristianini, John Shawe-Taylor, Jaz S. Kandola. 649-655 [doi]
• TAP Gibbs Free Energy, Belief Propagation and SparsityLehel Csató, Manfred Opper, Ole Winther. 657-663 [doi]
• Adaptive Nearest Neighbor Classification Using Support Vector MachinesCarlotta Domeniconi, Dimitrios Gunopulos. 665-672 [doi]
• Learning from Infinite Data in Finite TimePedro Domingos, Geoff Hulten. 673-680 [doi]
• A kernel method for multi-labelled classificationAndré Elisseeff, Jason Weston. 681-687 [doi]
• Approximate Dynamic Programming via Linear ProgrammingDaniela Pucci de Farias, Benjamin Van Roy. 689-695 [doi]
• Adaptive Sparseness Using Jeffreys PriorMário A. T. Figueiredo. 697-704 [doi]
• Incremental Learning and Selective Sampling via Parametric Optimization Framework for SVMShai Fine, Katya Scheinberg. 705-711 [doi]
• KLD-Sampling: Adaptive Particle FiltersDieter Fox. 713-720 [doi]
• Fast, Large-Scale Transformation-Invariant ClusteringBrendan J. Frey, Nebojsa Jojic. 721-727 [doi]
• Product Analysis: Learning to Model Observations as Products of Hidden VariablesBrendan J. Frey, Anitha Kannan, Nebojsa Jojic. 729-735 [doi]
• Very loopy belief propagation for unwrapping phase imagesBrendan J. Frey, Ralf Koetter, Nemanja Petrovic. 737-743 [doi]
• Discriminative Direction for Kernel ClassifiersPolina Golland. 745-752 [doi]
• Escaping the Convex Hull with Extrapolated Vector MachinesPatrick Haffner. 753-760 [doi]
• Kernel Feature Spaces and Nonlinear Blind Souce SeparationStefan Harmeling, Alexander Ziehe, Motoaki Kawanabe, Klaus-Robert Müller. 761-768 [doi]
• The Method of Quantum ClusteringDavid Horn, Assaf Gottlieb. 769-776 [doi]
• Active Information RetrievalTommi Jaakkola, Hava T. Siegelmann. 777-784 [doi]
• Online Learning with KernelsJyrki Kivinen, Alex J. Smola, Robert C. Williamson. 785-792 [doi]
• A Dynamic HMM for On-line Segmentation of Sequential DataJens Kohlmorgen, Steven Lemm. 793-800 [doi]
• Minimax Probability MachineGert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan. 801-807 [doi]
• (Not) Bounding the True ErrorJohn Langford, Rich Caruana. 809-816 [doi]
• An Efficient, Exact Algorithm for Solving Tree-Structured Graphical GamesMichael L. Littman, Michael J. Kearns, Satinder P. Singh. 817-823 [doi]
• Quantizing Density EstimatorsPeter Meinicke, Helge Ritter. 825-832 [doi]
• Linear-time inference in Hierarchical HMMsK. P. Murphy, Mark A. Paskin. 833-840 [doi]
• On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive BayesAndrew Y. Ng, Michael I. Jordan. 841-848 [doi]
• On Spectral Clustering: Analysis and an algorithmAndrew Y. Ng, Michael I. Jordan, Yair Weiss. 849-856 [doi]
• Learning Hierarchical Structures with Linear Relational EmbeddingAlberto Paccanaro, Geoffrey E. Hinton. 857-864 [doi]
• Matching Free Trees with Replicator EquationsMarcello Pelillo. 865-872 [doi]
• MIME: Mutual Information Minimization and Entropy Maximization for Bayesian Belief PropagationAnand Rangarajan, Alan L. Yuille. 873-880 [doi]
• Infinite Mixtures of Gaussian Process ExpertsCarl Edward Rasmussen, Zoubin Ghahramani. 881-888 [doi]
• Global Coordination of Local Linear ModelsSam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton. 889-896 [doi]
• Multiplicative Updates for Classification by Mixture ModelsLawrence K. Saul, Daniel D. Lee. 897-904 [doi]
• Covariance Kernels from Bayesian Generative ModelsMatthias Seeger. 905-912 [doi]
• Probabilistic Abstraction HierarchiesEran Segal, Daphne Koller, Dirk Ormoneit. 913-920 [doi]
• Dynamic Time-Alignment Kernel in Support Vector MachineHiroshi Shimodaira, K.-I. Noma, Mitsuru Nakai, Shigeki Sagayama. 921-928 [doi]
• Agglomerative Multivariate Information BottleneckNoam Slonim, Nir Friedman, Naftali Tishby. 929-936 [doi]
• Bayesian time series classificationPeter Sykacek, Stephen J. Roberts. 937-944 [doi]
• Partially labeled classification with Markov random walksMartin Szummer, Tommi Jaakkola. 945-952 [doi]
• The Unified Propagation and Scaling AlgorithmYee Whye Teh, Max Welling. 953-960 [doi]
• Risk Sensitive Particle FiltersSebastian Thrun, John Langford, Vandi Verma. 961-968 [doi]
• Learning Discriminative Feature Transforms to Low Dimensions in Low DimentionsKari Torkkola. 969-976 [doi]
• A New Discriminative Kernel From Probabilistic ModelsKoji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller. 977-984 [doi]
• K-Local Hyperplane and Convex Distance Nearest Neighbor AlgorithmsPascal Vincent, Yoshua Bengio. 985-992 [doi]
• Multi Dimensional ICA to Separate Correlated SourcesRoland Vollgraf, Klaus Obermayer. 993-1000 [doi]
• Tree-based reparameterization for approximate inference on loopy graphsMartin J. Wainwright, Tommi Jaakkola, Alan S. Willsky. 1001-1008 [doi]
• Learning Lateral Interactions for Feature Binding and Sensory SegmentationHeiko Wersing. 1009-1016 [doi]
• Products of GaussiansChristopher K. I. Williams, Felix V. Agakov, Stephen N. Felderhof. 1017-1024 [doi]
• Iterative Double Clustering for Unsupervised and Semi-Supervised LearningRan El-Yaniv, Oren Souroujon. 1025-1032 [doi]
• The Concave-Convex Procedure (CCCP)Alan L. Yuille, Anand Rangarajan. 1033-1040 [doi]
• Reducing multiclass to binary by coupling probability estimatesB. Zadrozny. 1041-1048 [doi]
• Blind Source Separation via Multinode Sparse RepresentationMichael Zibulevsky, Pavel Kisilev, Yehoshua Y. Zeevi, Barak A. Pearlmutter. 1049-1056 [doi]
• Spectral Relaxation for K-means ClusteringHongyuan Zha, Xiaofeng He, Chris H. Q. Ding, Ming Gu, Horst D. Simon. 1057-1064 [doi]
• A General Greedy Approximation Algorithm with ApplicationsT. Zhang. 1065-1072 [doi]
• EM-DD: An Improved Multiple-Instance Learning TechniqueQi Zhang, Sally A. Goldman. 1073-1080 [doi]
• Kernel Logistic Regression and the Import Vector MachineJi Zhu, Trevor Hastie. 1081-1088 [doi]
• Citcuits for VLSI Implementation of Temporally Asymmetric Hebbian LearningA. Bofill, A. F. Murray, D. P. Thompson. 1091-1098 [doi]
• Stochastic Mixed-Signal VLSI Architecture for High-Dimensional Kernel MachinesRoman Genov, Gert Cauwenberghs. 1099-1105 [doi]
• Orientation-Selective aVLSI Spiking NeuronsShih-Chii Liu, Jörg Kramer, Giacomo Indiveri, Tobi Delbrück, Rodney J. Douglas. 1107-1114 [doi]
• An Efficient Clustering Algorithm Using Stochastic Association Model and Its Implementation Using NanostructuresTakashi Morie, Tomohiro Matsuura, Makoto Nagata, Atsushi Iwata. 1115-1122 [doi]
• Learning Spike-Based Correlations and Conditional Probabilities in SiliconAaron P. Shon, David Hsu, Chris Diorio. 1123-1130 [doi]
• Analog Soft-Pattern-Matching Classifier using Floating-Gate MOS TechnologyToshihiko Yamasaki, Tadashi Shibata. 1131-1138 [doi]
• Intransitive Likelihood-Ratio ClassifiersJeff Bilmes, Gang Ji, Marina Meila. 1141-1148 [doi]
• Relative Density Nets: A New Way to Combine Backpropagation with HMM sAndrew D. Brown, Geoffrey E. Hinton. 1149-1156 [doi]
• A Sequence Kernel and its Application to Speaker RecognitionW. M. Campbell. 1157-1163 [doi]
• ALGONQUIN - Learning Dynamic Noise Models From Noisy Speech for Robust Speech RecognitionBrendan J. Frey, Trausti T. Kristjansson, Li Deng, Alex Acero. 1165-1171 [doi]
• Audio-Visual Sound Separation Via Hidden Markov ModelsJohn Hershey, Michael Casey. 1173-1180 [doi]
• Estimating the Reliability of ICA ProjectionsFrank C. Meinecke, Andreas Ziehe, Motoaki Kawanabe, Klaus-Robert Müller. 1181-1188 [doi]
• Speech Recognition with Missing Data using Recurrent Neural NetsS. Parveen, P. Green. 1189-1195 [doi]
• Speech Recognition using SVMsN. Smith, Mark J. F. Gales. 1197-1204 [doi]
• Sequential Noise Compensation by Sequential Monte Carlo MethodK. Yao, S. Nakamura. 1205-1212 [doi]
• A Neural Oscillator Model of Auditory Selective AttentionStuart N. Wrigley, Guy J. Brown. 1213-1220 [doi]
• Perceptual Metamers in Stereoscopic VisionB. T. Backus. 1223-1230 [doi]
• The g Factor: Relating Distributions on Features to Distributions on ImagesJames M. Coughlan, Alan L. Yuille. 1231-1238 [doi]
• Categorization by Learning and Combining Object PartsBernd Heisele, Thomas Serre, Massimiliano Pontil, Thomas Vetter, Tomaso Poggio. 1239-1245 [doi]
• Modeling the Modulatory Effect of Attention on Human Spatial VisionLaurent Itti, Jochen Braun, Christof Koch. 1247-1254 [doi]
• Grouping and dimensionality reduction by locally linear embeddingMarzia Polito, Pietro Perona. 1255-1262 [doi]
• Learning Body Pose via Specialized MapsRómer Rosales, Stan Sclaroff. 1263-1270 [doi]
• A Hierarchical Model of Complex Cells in Visual Cortex for the Binocular Perception of Motion-in-DepthSilvio P. Sabatini, Fabio Solari, G. Andreani, C. Bartolozzi, Giacomo M. Bisio. 1271-1278 [doi]
• The Fidelity of Local Ordinal EncodingJavid Sadr, Sayan Mukherjee, K. Thoresz, Pawan Sinha. 1279-1286 [doi]
• Unsupervised Learning of Human Motion ModelsYang Song, Luis Goncalves, Pietro Perona. 1287-1294 [doi]
• Transform-invariant Image Decomposition with Similarity TemplatesChris Stauffer, Erik G. Miller, Kinh Tieu. 1295-1302 [doi]
• Contextual Modulation of Target SaliencyAntonio B. Torralba. 1303-1310 [doi]
• Fast and Robust Classification using Asymmetric AdaBoost and a Detector CascadePaul A. Viola, Michael J. Jones. 1311-1318 [doi]
• A Rotation and Translation Invariant Discrete Saliency NetworkLance R. Williams, John W. Zweck. 1319-1326 [doi]
• Grouping with BiasStella X. Yu, Jianbo Shi. 1327-1334 [doi]
• Switch Packet Arbitration via Queue-LearningTimothy X. Brown. 1337-1344 [doi]
• Model Based Population Tracking and Automatic Detection of Distribution ChangesIgor V. Cadez, Paul S. Bradley. 1345-1352 [doi]
• Bayesian Predictive Profiles With Applications to Retail Transaction DataIgor V. Cadez, Padhraic Smyth. 1353-1360 [doi]
• Tempo tracking and rhythm quantization by sequential Monte CarloAli Taylan Cemgil, Bert Kappen. 1361-1368 [doi]
• Estimating Car Insurance Premia: a Case Study in High-Dimensional Data InferenceNicolas Chapados, Yoshua Bengio, Pascal Vincent, Joumana Ghosn, Charles Dugas, Ichiro Takeuchi, Linyan Meng. 1369-1376 [doi]
• Improvisation and LearningJudy A. Franklin. 1377-1384 [doi]
• Using Vocabulary Knowledge in Bayesian Multinomial EstimationThomas L. Griffiths, Joshua B. Tenenbaum. 1385-1392 [doi]
• Cobot: A Social Reinforcement Learning AgentCharles Lee Isbell Jr., Christian R. Shelton, Michael J. Kearns, Satinder P. Singh, Peter Stone. 1393-1400 [doi]
• Optimising Synchronisation Times for Mobile DevicesNeil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, M. J. Taylor. 1401-1408 [doi]
• Prodding the ROC Curve: Constrained Optimization of Classifier PerformanceMichael C. Mozer, Robert H. Dodier, Michael D. Colagrosso, Cesar Guerra-Salcedo, Richard H. Wolniewicz. 1409-1415 [doi]
• Hyperbolic Self-Organizing Maps for Semantic NavigationJörg Ontrup, Helge Ritter. 1417-1424 [doi]
• Learning a Gaussian Process Prior for Automatically Generating Music PlaylistsJohn C. Platt, Christopher J. C. Burges, S. Swenson, C. Weare, A. Zheng. 1425-1432 [doi]
• A Bayesian Network for Real-Time Musical AccompanimentChristopher Raphael. 1433-1439 [doi]
• The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRankMatthew Richardson, Pedro Domingos. 1441-1448 [doi]
• Active Learning in the Drug Discovery ProcessManfred K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen. 1449-1456 [doi]
• Face Recognition Using Kernel MethodsMing-Hour Yang. 1457-1464 [doi]
• Active Portfolio-Management based on Error Correction Neural NetworksHans-Georg Zimmermann, Ralph Neuneier, Ralph Grothmann. 1465-1472 [doi]
• Reinforcement Learning with Long Short-Term MemoryBram Bakker. 1475-1482 [doi]
• Playing is believing: The role of beliefs in multi-agent learningYu-Han Chang, Leslie Pack Kaelbling. 1483-1490 [doi]
• Batch Value Function Approximation via Support VectorsThomas G. Dietterich, Xin Wang. 1491-1498 [doi]
• Convergence of Optimistic and Incremental Q-LearningEyal Even-Dar, Yishay Mansour. 1499-1506 [doi]
• Variance Reduction Techniques for Gradient Estimates in Reinforcement LearningEvan Greensmith, Peter L. Bartlett, Jonathan Baxter. 1507-1514 [doi]
• Rates of Convergence of Performance Gradient Estimates Using Function Approximation and Bias in Reinforcement LearningGregory Z. Grudic, Lyle H. Ungar. 1515-1522 [doi]
• Multiagent Planning with Factored MDPsCarlos Guestrin, Daphne Koller, Ronald Parr. 1523-1530 [doi]
• A Natural Policy GradientSham Kakade. 1531-1538 [doi]
• Incremental A*Sven Koenig, Maxim Likhachev. 1539-1546 [doi]
• Model-Free Least-Squares Policy IterationMichail G. Lagoudakis, Ronald Parr. 1547-1554 [doi]
• Predictive Representations of StateMichael L. Littman, Richard S. Sutton, Satinder P. Singh. 1555-1561 [doi]
• The Steering Approach for Multi-Criteria Reinforcement LearningShie Mannor, Nahum Shimkin. 1563-1570 [doi]
• Efficient Resources Allocation for Markov Decision ProcessesRémi Munos. 1571-1578 [doi]
• Direct value-approximation for factored MDPsDale Schuurmans, Relu Patrascu. 1579-1586 [doi]
• Stabilizing Value Function Approximation with the BFBP AlgorithmXin Wang, Thomas G. Dietterich. 1587-1594 [doi]