NIPS 1998: Denver, CO, USA

Michael J. Kearns, Sara A. Solla, David A. Cohn (Eds.): Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30 - December 5, 1998]. The MIT Press 1999, ISBN 0-262-11245-0 BibTeX

@proceedings{DBLP:conf/nips/1998,
  editor    = {Michael J. Kearns and
               Sara A. Solla and
               David A. Cohn},
  title     = {Advances in Neural Information Processing Systems 11, [NIPS Conference,
               Denver, Colorado, USA, November 30 - December 5, 1998]},
  booktitle = {NIPS},
  publisher = {The MIT Press},
  year      = {1999},
  isbn      = {0-262-11245-0},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Cognitive Science

Nikhil Bhushan, Reza Shadmehr:
Evidence for a Forward Dynamics Model in Human Adaptive Motor Control. 3-9
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Ke Chen, DeLiang L. Wang:
Perceiving without Learning: From Spirals to Inside/Outside Relations. 10-16
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G. Bjorn Christianson, Suzanna Becker:
A Model for Associative Multiplication. 17-23
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Matthew N. Dailey, Garrison W. Cottrell, Thomas A. Busey:
Facial Memory Is Kernel Density Estimation (Almost). 24-30
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Masahiko Haruno, Daniel M. Wolpert, Mitsuo Kawato:
Multiple Paired Forward-Inverse Models for Human Motor Learning and Control. 31-37
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Bradley C. Love:
Utilizing lime: Asynchronous Binding. 38-44
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Zili Liu, Daphna Weinshall:
Mechanisms of Generalization in Perceptual Learning. 45-51
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Michael Mozer:
A Principle for Unsupervised Hierarchical Decomposition of Visual Scenes. 52-58
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Neuroscience

Joshua B. Tenenbaum:
Bayesian Modeling of Human Concept Learning. 59-68
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L. F. Abbott, Sen Song:
Temporally Asymmetric Hebbian Learning, Spike liming and Neural Response Variability. 69-75
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Péter Adorján, Klaus Obermayer:
Contrast Adaptation in Simple Cells by Changing the Transmitter Release Probability. 76-82
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Pierre Baraduc, Emmanuel Guigon, Yves Burnod:
Where Does the Population Vector of Motor Cortical Cells Point during Reaching Movements? 83-89
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Frances S. Chance, Sacha B. Nelson, L. F. Abbott:
Recurrent Cortical Amplification Produces Complex Cell Responses. 90-96
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Gal Chechik, Isaac Meilijson, Eytan Ruppin:
Neuronal Regulation Implements Efficient Synaptic Pruning. 97-103
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Sophie Deneve, Alexandre Pouget, Peter E. Latham:
Divisive Normalization, Line Attractor Networks and Ideal Observers. 104-110
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Itay Gat, Naftali Tishby:
Synergy and Redundancy among Brain Cells of Behaving Monkeys. 111-117
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Tzyy-Ping Jung, Scott Makeig, Marissa Westerfield, Jeanne Townsend, Eric Courchesne, Terrence J. Sejnowski:
Analyzing and Visualizing Single-Trial Event-Related Potentials. 118-124
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Richard Kempter, Wulfram Gerstner, J. Leo van Hemmen:
Spike-Based Compared to Rate-Based Hebbian Learning. 125-131
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Amit Manwani, Christof Koch:
Signal Detection in Noisy Weakly-Active Dendrites. 132-138
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Christian Piepenbrock, Klaus Obermayer:
The Role of Lateral Cortical Competition in Ocular Dominance Development. 139-145
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Dmitry Rinberg, Hanan Davidowitz, Naftali Tishby:
Multi-Electrode Spike Sorting by Clustering Transfer Functions. 146-152
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Eero P. Simoncelli, Odelia Schwartz:
Modeling Surround Suppression in V1 Neurons with a Statistically Derived Normalization Model. 153-159
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Martin Stemmler, Christof Koch:
Information Maximization in Single Neurons. 160-166
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Hyoungsoo Yoon, Haim Sompolinsky:
The Effect of Correlations on the Fisher Information of Population Codes. 167-173
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Theory

Richard S. Zemel, Peter Dayan:
Distributional Population Codes and Multiple Motion Models. 174-182
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David Barber, Wim Wiegerinck:
Tractable Variational Structures for Approximating Graphical Models. 183-189
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Peter L. Bartlett, Vitaly Maiorov, Ron Meir:
Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks. 190-196
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Anthony C. C. Coolen, David Saad:
Dynamics of Supervised Learning with Restricted Training Sets. 197-203
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Nello Cristianini, Colin Campbell, John Shawe-Taylor:
Dynamically Adapting Kernels in Support Vector Machines. 204-210
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A. Düring, Anthony C. C. Coolen, D. Sherrington:
Phase Diagram and Storage Capacity of Sequence-Storing Neural Networks. 211-217
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Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper:
Finite-Dimensional Approximation of Gaussian Processes. 218-224
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Claudio Gentile, Manfred K. Warmuth:
Linear Hinge Loss and Average Margin. 225-231
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Didier Herschkowitz, Jean-Pierre Nadal:
Unsupervised and Supervised Clustering: The Mutual Information between Parameters and Observations. 232-238
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Shiro Ikeda, Shun-ichi Amari, Hiroyuki Nakahara:
Convergence of the Wake-Sleep Algorithm. 239-245
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Yoshiyuki Kabashima, David Saad:
The Belief in TAP. 246-252
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Grigoris J. Karakoulas, John Shawe-Taylor:
Optimizing Classifers for Imbalanced Training Sets. 253-259
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Michael J. Kearns, Lawrence K. Saul:
Inference in Multilayer Networks via Large Deviation Bounds. 260-266
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Friedrich Leisch, Adrian Trapletti, Kurt Hornik:
Stationarity and Stability of Autoregressive Neural Network Processes. 267-273
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Zhaoping Li, Peter Dayan:
Computational Differences between Asymmetrical and Symmetrical Networks. 274-280
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Wolfgang Maass, Eduardo D. Sontag:
A Precise Characterization of the Class of Languages Recognized by Neural Nets under Gaussian and Other Common Noise Distributions. 281-287
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Llew Mason, Peter L. Bartlett, Jonathan Baxter:
Direct Optimization of Margins Improves Generalization in Combined Classifiers. 288-294
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Ron Meir, Vitaly Maiorov:
On the Optimality of Incremental Neural Network Algorithms. 295-301
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Manfred Opper, Francesco Vivarelli:
General Bounds on Bayes Errors for Regression with Gaussian Processes. 302-308
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Manfred Opper, Ole Winther:
Mean Field Methods for Classification with Gaussian Processes. 309-315
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H. C. Rae, Peter Sollich, Anthony C. C. Coolen:
On-Line Learning with Restricted Training Sets: Exact Solution as Benchmark for General Theories. 316-322
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Akito Sakurai:
Tight Bounds for the VC-Dimension of Piecewise Polynomial Networks. 323-329
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Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson:
Shrinking the Tube: A New Support Vector Regression Algorithm. 330-336
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N. S. Skantzos, C. F. Beckmann, Anthony C. C. Coolen:
Discontinuous Recall Transitions Induced by Competition Between Short- and Long-Range Interactions in Recurrent Networks. 337-345
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Peter Sollich:
Learning Curves for Gaussian Processes. 344-350
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Algorithms and Architecture

Toshiyuki Tanaka:
A Theory of Mean Field Approximation. 351-360
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Hagai Attias:
Learning a Hierarchical Belief Network of Independent Factor Analyzers. 361-367
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Kristin P. Bennett, Ayhan Demiriz:
Semi-Supervised Support Vector Machines. 368-374
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Mauro Birattari, Gianluca Bontempi, Hugues Bersini:
Lazy Learning Meets the Recursive Least Squares Algorithm. 375-381
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Christopher M. Bishop:
Bayesian PCA. 382-388
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Andrew Blake, Ben North, Michael Isard:
Learning Multi-Class Dynamics. 389-395
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Xavier Boyen, Daphne Koller:
Approximate Learning of Dynamic Models. 396-402
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Thomas Briegel, Volker Tresp:
Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models. 403-409
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João F. G. de Freitas, Mahesan Niranjan, Arnaud Doucet, Andrew H. Gee:
Global Optimisation of Neural Network Models via Sequential Sampling. 410-416
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Nir Friedman, Yoram Singer:
Efficient Bayesian Parameter Estimation in Large Discrete Domains. 417-423
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Yoram Gdalyahu, Daphna Weinshall, Michael Werman:
A Randomized Algorithm for Pairwise Clustering. 424-430
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Zoubin Ghahramani, Sam T. Roweis:
Learning Nonlinear Dynamical Systems Using an EM Algorithm. 431-437
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Thore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, Klaus Obermayer:
Classification on Pairwise Proximity Data. 438-444
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Yves Grandvalet, Stéphane Canu:
Outcomes of the Equivalence of Adaptive Ridge with Least Absolute Shrinkage. 445-451
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Marcus Held, Jan Puzicha, Joachim M. Buhmann:
Visualizing Group Structure. 452-458
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Sepp Hochreiter, Jürgen Schmidhuber:
Source Separation as a By-Product of Regularization. 459-465
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Thomas Hofmann, Jan Puzicha, Michael I. Jordan:
Learning from Dyadic Data. 466-472
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Aapo Hyvärinen, Patrik O. Hoyer, Erkki Oja:
Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation. 473-479
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Charles Lee Isbell Jr., Paul A. Viola:
Restructuring Sparse High Dimensional Data for Effective Retrieval. 480-486
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Tommi Jaakkola, David Haussler:
Exploiting Generative Models in Discriminative Classifiers. 487-493
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Tony Jebara, Alex Pentland:
Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm. 494-500
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Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger:
A Polygonal Line Algorithm for Constructing Principal Curves. 501-507
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Te-Won Lee, Michael S. Lewicki, Terrence J. Sejnowski:
Unsupervised Classification with Non-Gaussian Mixture Models Using ICA. 508-514
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Daniel D. Lee, Haim Sompolinsky:
Learning a Continuous Hidden Variable Model for Binary Data. 515-521
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Malik Magdon-Ismail, Amir F. Atiya:
Neural Networks for Density Estimation. 522-528
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Alan D. Marrs, Andrew R. Webb:
Exploratory Data Analysis Using Radial Basis Function Latent Variable Models. 529-535
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Sebastian Mika, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch:
Kernel PCA and De-Noising in Feature Spaces. 536-542
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Andrew W. Moore:
Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees. 543-549
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Marcello Pelillo:
Replicator Equations, Maximal Cliques, and Graph Isomorphism. 550-556
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John C. Platt:
Using Analytic QP and Sparseness to Speed Training of Support Vector Machines. 557-563
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Gunnar Rätsch, Takashi Onoda, Klaus-Robert Müller:
Regularizing AdaBoost. 564-570
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Patrice Simard, Léon Bottou, Patrick Haffner, Yann LeCun:
Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks. 571-577
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Yoram Singer, Manfred K. Warmuth:
Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy. 578-584
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Alex J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf:
Semiparametric Support Vector and Linear Programming Machines. 585-591
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Michael E. Tipping:
Probabilistic Visualisation of High-Dimensional Binary Data. 592-598
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Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton:
SMEM Algorithm for Mixture Models. 599-605
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Nuno Vasconcelos, Andrew Lippman:
Learning Mixture Hierarchies. 606-612
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Francesco Vivarelli, Christopher K. I. Williams:
Discovering Hidden Features with Gaussian Processes Regression. 613-619
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Grace Wahba, Xiwu Lin, Fangyu Gao, Dong Xiang, Ronald Klein, Barbara Klein:
The Bias-Variance Tradeoff and the Randomized GACV. 620-626
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Kevin R. Wheeler, Atam P. Dhawan:
Basis Selection for Wavelet Regression. 627-633
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Christopher K. I. Williams, Nicholas J. Adams:
DTs: Dynamic Trees. 634-640
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Alan L. Yuille, James M. Coughlan:
Convergence Rates of Algorithms for Visual Search: Detecting Visual Contours. 641-647
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Implementation

Liqing Zhang, Andrzej Cichocki:
Blind Separation of Filtered Sources Using State-Space Approach. 648-656
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Gert Cauwenberghs, James Waskiewicz:
Analog VLSI Cellular Implementation of the Boundary Contour System. 657-663
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Jung-Wook Cho, Soo-Young Lee:
Active Noise Canceling Using Analog Neuro-Chip with On-Chip Learning Capability. 664-670
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Richard Coggins, Raymond J. Wang, Marwan A. Jabri:
A Micropower CMOS Adaptive Amplitude and Shift Invariant Vector Quantiser. 671-677
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R. Timothy Edwards, Gert Cauwenberghs, Fernando J. Pineda:
Optimizing Correlation Algorithms for Hardware-Based Transient Classification. 678-684
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Ralph Etienne-Cummings, Viktor Gruev, Mohammed Abdel Ghani:
VLSI Implementation of Motion Centroid Localization for Autonomous Navigation. 685-691
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John G. Harris, Chiang-Jung Pu, José Carlos Príncipe:
A Neuromorphic Monaural Sound Localizer. 692-698
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Charles M. Higgins, Christof Koch:
An Integrated Vision Sensor for the Computation of Optical Flow Singular Points. 699-705
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Alan Stocker, Rodney J. Douglas:
Computation of Smooth Optical Flow in a Feedback Connected Analog Network. 706-712
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Speech, Handwriting and Signal Processing

Ping Zhou, Jim Austin, John Kennedy:
A High Performance k-NN Classifier Using a Binary Correlation Matrix Memory. 713-722
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Matthew Brand:
An Entropic Estimator for Structure Discovery. 723-729
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Michael S. Lewicki, Terrence J. Sejnowski:
Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations. 730-736
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Christoph Neukirchen, Gerhard Rigoll:
Controlling the Complexity of HMM Systems by Regularization. 737-743
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David A. Nix, John E. Hogden:
Maximum-Likelihood Continuity Mapping (MALCOM): An Alternative to HMMs. 744-750
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Visual Processing

Lawrence K. Saul, Mazin G. Rahim:
Markov Processes on Curves for Automatic Speech Recognition. 751-760
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James M. Coughlan, Alan L. Yuille:
A Phase Space Approach to Minimax Entropy Learning and the Minutemax Approximations. 761-767
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Trevor Darrell:
Example-Based Image Synthesis of Articulated Figures. 768-774
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William T. Freeman, Egon C. Pasztor:
Learning to Estimate Scenes from Images. 775-781
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Sergey Ioffe, David A. Forsyth:
Learning to Find Pictures of People. 782-788
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Laurent Itti, Jochen Braun, Dale K. Lee, Christof Koch:
Attentional Modulation of Human Pattern Discrimination Psychophysics Reproduced by a Quantitative Model. 789-795
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Zhaoping Li:
A V1 Model of Pop Out and Asymmetty in Visual Search. 796-802
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P. Jonathon Phillips:
Support Vector Machines Applied to Face Recognition. 803-809
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Rajesh P. N. Rao, Daniel L. Ruderman:
Learning Lie Groups for Invariant Visual Perception. 810-816
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Ruth Rosenholtz:
General-Purpose Localization of Textured Image Regions. 817-823
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Ravi K. Sharma, Todd K. Leen, Misha Pavel:
Probabilistic Image Sensor Fusion. 824-830
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Karvel K. Thornber, Lance R. Williams:
Orientation, Scale, and Discontinuity as Emergent Properties of Illusory Contour Shape. 831-837
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Applications

Daphna Weinshall, David W. Jacobs, Yoram Gdalyahu:
Classification in Non-Metric Spaces. 838-846
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Shumeet Baluja:
Making Templates Rotationally Invariant. An Application to Rotated Digit Recognition. 847-853
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Shumeet Baluja:
Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data. 854-860
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Dan Cornford, Ian T. Nabney, Christopher K. I. Williams:
Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields. 861-867
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Gideon Dror, Halina Abramowicz, David Horn:
Vertex Identification in High Energy Physics Experiments. 868-874
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Eric Granger, Stephen Grossberg, Mark A. Rubin, William W. Streilein:
Familiarity Discrimination of Radar Pulses. 875-881
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Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton:
Fast Neural Network Emulation of Dynamical Systems for Computer Animation. 882-888
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Jaakko Hollmén, Volker Tresp:
Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model. 889-895
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Benoit Huet, Andrew D. J. Cross, Edwin R. Hancock:
Graph Matching for Shape Retrieval. 896-902
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Amy McGovern, J. Eliot B. Moss:
Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts. 903-909
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Baback Moghaddam, Tony Jebara, Alex Pentland:
Bayesian Modeling of Facial Similarity. 910-916
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John E. Moody, Matthew Saffell:
Reinforcement Learning for Trading. 917-923
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Nuria Oliver, Barbara Rosario, Alex Pentland:
Graphical Models for Recognizing Human Interactions. 924-930
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Klaus Prank, Julia Börger, Alexander von zur Mühlen, Georg Brabant, Christof Schöfl:
Independent Component Analysis of Intracellular Calcium Spike Data. 931-937
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Clay Spence, Paul Sajda:
Applications of Multi-Resolution Neural Networks to Mammography. 938-944
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Matthew M. Williamson, Roderick Murray-Smith, Volker Hansen:
Robot Docking Using Mixtures of Gaussians. 945-951
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Control, Navigation and Planning

David Wolpert, Kagan Tumer, Jeremy Frank:
Using Collective Intelligence to Route Internet Traffic. 952-960
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Mohammad A. Al-Ansari, Ronald J. Williams:
Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm. 961-967
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Leemon C. Baird III, Andrew W. Moore:
Gradient Descent for General Reinforcement Learning. 968-974
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Lyndon J. Brown, Gregory E. Gonye, James S. Schwaber:
Non-Linear PI Control Inspired by Biological Control Systems. 975-981
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Timothy X. Brown, Hui Tong, Satinder P. Singh:
Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning. 982-988
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Akira Hayashi, Nobuo Suematsu:
Viewing Classifier Systems as Model Free Learning in POMDPs. 989-995
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Michael J. Kearns, Satinder P. Singh:
Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms. 996-1002
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Sven Koenig:
Exploring Unknown Environments with Real-Time Search or Reinforcement Learning. 1003-1009
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John Loch:
The Effect of Eligibility Traces on Finding Optimal Memoryless Policies in Partially Observable Markov Decision Processes. 1010-1016
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Robert Moll, Andrew G. Barto, Theodore J. Perkins, Richard S. Sutton:
Learning Instance-Independent Value Functions to Enhance Local Search. 1017-1023
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Rémi Munos, Andrew W. Moore:
Barycentric Interpolators for Continuous Space and Time Reinforcement Learning. 1024-1030
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Ralph Neuneier, Oliver Mihatsch:
Risk Sensitive Reinforcement Learning. 1031-1037
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Eimei Oyama, Susumu Tachi:
Coordinate Transformation Learning of Hand Position Feedback Controller by Using Change of Position Error Norm. 1038-1044
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Jette Randlov:
Learning Macro-Actions in Reinforcement Learning. 1045-1051
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Masa-aki Sato, Shin Ishii:
Reinforcement Learning Based on On-Line EM Algorithm. 1052-1058
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Nobuo Suematsu, Akira Hayashi:
A Reinforcement Learning Algorithm in Partially Observable Environments Using Short-Term Memory. 1059-1065
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Richard S. Sutton, Satinder P. Singh, Doina Precup, Balaraman Ravindran:
Improved Switching among Temporally Abstract Actions. 1066-1072
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John K. Williams, Satinder P. Singh:
Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision Processes. 1073-1080
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