Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference (Hardcover) (神經資訊處理系統進展 16：2003年會議論文集 (精裝版))

Description:

The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees -- physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.

Sebastian Thrun is Associate Professor in the Computer Science Department at Stanford University and Director of the Stanford AI Lab.

Lawrence K. Saul is Assistant Professor in the Department of Computer and Information Science at the University of Pennsylvania and General Chair of the 2004 NIPS conference.

Bernhard Schölkopf is Director at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, and Professor at the Technical University in Berlin.

 

 

Table of Contents:

Preface	xvii
	NIPS Committees	xxi
	Reviewers	xxiii
I	Algorithms and Architectures
	Efficient Multiscale Sampling from Products of Gaussian Mixtures Alexander T. Ihler, Erik B. Sudderth, William T. Freeman and Alan S. Willsky	1
	Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles Mark Girolami and Ata Kaban	9
	Hierarchical Topic Models and the Nested Chinese Restaurant Process David M. Blei, Thomas L. Griffiths, Joshua B. Tenenbaum and Michael I. Jordan	17
	Max-Margin Markov Networks Benjamin Taskar, Carlos Guestrin and Daphne Koller	25
	Invariant Pattern Recognition by Semi-Definite Programming Machines Thore Graepel and Ralf Herbrich	33
	Learning a Distance Metric from Relative Comparisons Matthew Schultz and Thorsten Joachims	41
	1-norm Support Vector Machines Ji Zhu, Saharon Rosset, Trevor Hastie and Robert Tibshirani	49
	Image Reconstruction by Linear Programming Koji Tsuda and Gunnar Rätsch	57
	Multiple-Instance Learning via Disjunctive Programming Boosting Stuart Andrews and Thomas Hofmann	65
	Convex Methods for Transduction Tijl De Bie and Nello Cristianini	73
	Kernel Dimensionality Reduction for Supervised Learning Kenji Fukumizu, Francis R. Bach and Michael I. Jordan	81
	Clustering with the Connectivity Kernel Bernd Fischer, Volker Roth and Joachim M. Buhmann	89
	Efficient and Robust Feature Extraction by Maximum Margin Criterion Haifeng Li, Tao Jiang and Keshu Zhang	97
	Sparse Greedy Minimax Probability Machine Classification Thomas Strohmann, Andrei Belitski, Greg Grudic and Dennis DeCoste	105
	Sequential Bayesian Kernel Regression Jaco Vermaak, Simon J. Godsill and Arnaud Doucet	113
	Fast Feature Selection from Microarray Expression Data via Multiplicative Large Margin Algorithms Claudio Gentile	121
	Dynamical Modeling with Kernels for Nonlinear Time Series Prediction Liva Ralaivola and Florence d'Alché-Buc	129
	Extreme Components Analysis Max Welling, Felix Agakov and Christopher K. I. Williams	137
	Linear Dependent Dimensionality Reduction Nathan Srebro and Tommi Jaakkola	145
	Locality Preserving Projections Xiaofei He and Partha Niyogi	153
	Optimal Manifold Representation of Data: An Information Theoretic Approach Denis V. Chigirev and William Bialek	161
	Ranking on Data Manifolds Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet and Bernhard Schölkopf	169
	Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering Yoshua Bengio, Jean-François Paiement, Pascal Vincent, Olivier Delalleau, Nicolas Le Roux and Marie Ouimet	177
	Pairwise Clustering and Graphical Models Noam Shental, Assaf Zomet, Tomer Hertz and Yair Weiss	185
	Tree-structured Approximations by Expectation Propagation Thomas Minka and Yuan Qi	193
	The IM Algorithm: A Variational Approach to Information Maximization David Barber and Felix Agakov	201
	Iterative Scaled Trust-Region Learning in Krylov Subspaces via Pearlmutter's Implicit Sparse Hessian-Vector Multiply Eiji Mizutani and James W. Demmel	209
	Large Scale Online Learning Léon Bottou and Yann Le Cun	217
	Online Classification on a Budget Koby Crammer, Jaz Kandola and Yoram Singer	225
	Online Learning via Global Feedback for Phrase Recognition Xavier Carreras and Lluis Marquez	233
	Sparse Representation and Its Applications in Blind Source Separation Yuanqing Li, Andrzej Cichocki, Shun-ichi Amari, Sergei Shishkin, Jianting Cao and Fanji Gu	241
	Perspectives on Sparse Bayesian Learning David Wipf, Jason Palmer and Bhaskar D. Rao	249
	Semi-Supervised Learning with Trees Charles Kemp, Thomas L. Griffiths, Sean Stromsten and Joshua B. Tenenbaum	257
	Efficient Exact k-NN and Nonparametric Classification in High Dimensions Ting Liu, Andrew W. Moore and Alexander Gray	265
	Nonstationary Covariance Functions for Gaussian Process Regression Christopher J. Paciorek and Mark J. Schervish	273
	Learning the k in k-means Greg Hamerly and Charles Elkan	281
	Finding the M Most Probable Configurations in Arbitrary Graphical Models Chen Yanover and Yair Weiss	289
	Non-linear CCA and PCA by Alignment of Local Models Jakob J. Verbeek, Sam T. Roweis and Nikos Vlassis	297
	Learning Spectral Clustering Francis R. Bach and Michael I. Jordan	305
	AUC Optimization vs. Error Rate Minimization Corinna Cortes and Mehryar Mohri	313
	Learning with Local and Global Consistency Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston and Bernhard Schölkopf	321
	Gaussian Process Latent Variable Models for Visualization of High Dimensional Data Neil D. Lawrence	329
	Warped Gaussian Processes Edward Snelson, Carl Edward Rasmussen and Zoubin Ghahramani	337
	Can We Learn to Beat the Best Stock Allan Borodin, Ran El-Yaniv and Vincent Gogan	345
	Approximate Expectation Maximization Tom Heskes, Onno Zoeter and Wim Wiegerinck	353
	Linear Response for Approximate Inference Max Welling and Yee Whye Teh	361
	Semidefinite Relaxations for Approximate Inference on Graphs with Cycles Martin Wainwright and Michael I. Jordan	369
	Approximability of Probability Distributions Alina Beygelzimer and Irina Rish	377
	Denoising and Untangling Graphs Using Degree Priors Quaid D. Morris and Brendan J. Frey	385
	On the Concentration of Expectation and Approximate Inference in Layered Networks XuanLong Nguyen and Michael I. Jordan	393
	Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models Radford M. Neal, Matthew J. Beal and Sam T. Roweis	401
	Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis Pedro F. Felzenszwalb, Daniel P. Huttenlocher and Jon M. Kleinberg	409
	Wormholes Improve Contrastive Divergence Geoffrey Hinton, Max Welling and Andriy Mnih	417
	Sample Propagation Mark A. Paskin	425
	Generalised Propagation for Fast Fourier Transforms with Partial or Missing Data Amos J. Storkey	433
	Laplace Propagation Alexander Smola, Vishy Vishwanathan and Eleazar Eskin	441
	Learning to Find Pre-Images Geokhan H. Bakir, Jason Weston and Bernhard Schölkopf	449
	Semi-Definite Programming by Perceptron Learning Thore Graepel, Ralf Herbrich, Andriy Kharechko and John Shawe-Taylor	457
	Computing Gaussian Mixture Models with EM Using Equivalence Constraints Noam Shental, Aharon Bar-Hillel, Tomer Hertz and Daphna Weinshall	465
	Feature Selection in Clustering Problems Volker Roth and Tilman Lange	473
	An Iterative Improvement Procedure for Hierarchical Clustering David Kauchak and Sanjoy Dasgupta	481
	Identifying Structure across Pre-Partitioned Data Zvika Marx, Ido Dagan and Eli Shamir	489
	Log-Linear Models for Label Ranking Ofer Dekel, Christopher Manning and Yoram Singer	497
	Minimax Embeddings Matthew Brand	505
	No Unbiased Estimator of the Variance of K-Fold Cross-Validation Yoshua Bengio and Yves Grandvalet	513
	Bias-Corrected Bootstrap and Model Uncertainty Harald Steck and Tommi Jaakkola	521
	Probability Estimates for Multi-Class Classification by Pairwise Coupling Ting-Fan Wu, Chih-Jen Lin and Ruby C. Weng	529
	Necessary Intransitive Likelihood-Ratio Classifiers Gang Ji and Jeff Bilmes	537
	Classification with Hybrid Generative/Discriminative Models Rajat Raina, Yirong Shen, Andrew Y. Ng and Andrew McCallum	545
	A Model for Learning the Semantics of Pictures Victor Lavrenko, R. Manmatha and Jiwoon Jeon	553
	Algorithms for Interdependent Security Games Michael Kearns and Luis Ortiz	561
II	Applications
	Fast Embedding of Sparse Similarity Graphs John C. Platt	571
	GPPS: A Gaussian Process Positioning System for Cellular Networks Anton Schwaighofer, Marian Grigoras, Volker Tresp and Clemens Hoffmann	579
	An Autonomous Robotic System for Mapping Abandoned Mines David Ferguson, Aaron Morris, Dirk Hähnel, Christopher Baker, Zachary Omohundro, Carlos Reverte, Scott Thayer, William Whittaker, Wolfram Burgard and Sebastian Thrun	587
	Semi-supervised Protein Classification Using Cluster Kernels Jason Weston, Christina Leslie, Dengyong Zhou, André Elisseeff and William S. Noble	595
	Statistical Debugging of Sampled Programs Alice X. Zheng, Michael I. Jordan, Ben Liblit and Alex Aiken	603
	Markov Models for Automated ECG Interval Analysis Nicholas P. Hughes, Lionel Tarassenko and Stephen Roberts	611
	Parameterized Novelty Detectors for Environmental Sensor Monitoring Cynthia Archer, Todd K. Leen and Antonio Baptista	619
	Modeling User Rating Profiles for Collaborative Filtering Benjamin Marlin	627
	Application of SVMs for Colour Classification and Collision Detection with AIBO Robots Michael J. Quinlan, Stephan K. Chalup and Richard H. Middleton	635
	Kernels for Structured Natural Language Data Jun Suzuki, Yutaka Sasaki and Eisaku Maeda	643
	A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters Daniel B. Neill and Andrew W. Moore	651
	Link Prediction in Relational Data Benjamin Taskar, Ming-Fai Wong, Pieter Abbeel and Daphne Koller	659
	Unsupervised Color Decomposition of Histologically Stained Tissue Samples Andrew Rabinovich, Sameer Agarwal, Casey Laris, Jeffrey H. Price and Serge J. Belongie	667
	ICA-based Clustering of Genes from Microarray Expression Data Su-In Lee and Serafim Batzoglou	675
	Gene Expression Clustering with Functional Mixture Models Darya Chudova, Christopher Hart, Eric Mjolsness and Padhraic Smyth	683
III	Brain Imaging
	Reconstructing MEG Sources with Unknown Correlations Maneesh Sahani and Srikantan Nagarajan	693
	Different Cortico-Basal Ganglia Loops Specialize in Reward Prediction at Different Time Scales Saori C. Tanaka, Kenji Doya, Go Okada, Kazutaka Ueda, Yasumasa Okamoto and Shigeto Yamawaki	701
	Training fMRI Classifiers to Discriminate Cognitive States across Multiple Subjects Xuerui Wang, Rebecca Hutchinson and Tom M. Mitchell	709
	Nonlinear Filtering of Electron Micrographs by Means of Support Vector Regression Roland Vollgraf, Michael Scholz, Ian A. Meinertzhagen and Klaus Obermayer	717
	Impact of an Energy Normalization Transform on the Performance of the LF-ASD Brain Computer Interface Yu Zhou, Steven G. Mason and Gary E. Birch	725
	Increase Information Transfer Rates in BCI by CSP Extension to Multi-class Guido Dornhege, Benjamin Blankertz, Gabriel Curio and Klaus-Robert Müller	733
	Subject-Independent Magnetoencephalographic Source Localization by a Multilayer Perceptron Sung C. Jun and Barak A. Pearlmutter	741
IV	Control and Reinforcement Learning
	Gaussian Processes in Reinforcement Learning Carl Edward Rasmussen and Malte Kuss	751
	Applying Metric-Trees to Belief-Point POMDPs Jolette Pineau, Geoffrey J. Gordon and Sebastian Thrun	759
	ARA*: Anytime A* with Provable Bounds on Sub-Optimality Maxim Likhachev, Geoffrey J. Gordon and Sebastian Thrun	767
	Approximate Planning in POMDPs with Macro-Actions Georgios Theocharous and Leslie Pack Kaelbling	775
	Envelope-based Planning in Relational MDPs Natalia H. Gardiol and Leslie Pack Kaelbling	783
	An MDP-Based Approach to Online Mechanism Design David C. Parkes and Satinder P. Singh	791
	Autonomous Helicopter Flight via Reinforcement Learning Andrew Y. Ng, H. Jin Kim, Michael I. Jordan and Shankar Sastry	799
	All learning is Local: Multi-agent Learning in Global Reward Games Yu-Han Chang, Tracey Ho and Leslie Pack Kaelbling	807
	How to Combine Expert (and Novice) Advice when Actions Impact the Environment? Daniela Pucci de Farias and Nimrod Megiddo	815
	Bounded Finite State Controllers Pascal Poupart and Craig Boutilier	823
	Policy Search by Dynamic Programming J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng and Jeff Schneider	831
	Robustness in Markov Decision Problems with Uncertain Transition Matrices Arnab Nilim and Laurent El Ghaoui	839
	Approximate Policy Iteration with a Policy Language Bias Alan Fern, Sungwook Yoon and Robert Givan	847
	A Nonlinear Predictive State Representation Matthew R. Rudary and Satinder P. Singh	855
	Learning Near-Pareto-Optimal Conventions in Polynomial Time XiaoFeng Wang and Tuomas Sandholm	863
	Extending Q-Learning to General Adaptive Multi-Agent Systems Gerald Tesauro	871
	Auction Mechanism Design for Multi-Robot Coordination Curt Bererton, Geoffrey J. Gordon and Sebastian Thrun	879
	Distributed Optimization in Adaptive Networks Ciamac C. Moallemi and Benjamin Van Roy	887
	Linear Program Approximations for Factored Continuous-State Markov Decision Processes Milos Hauskrecht and Branislav Kveton	895
V	Cognitive Science and Artificial Intelligence
	Insights from Machine Learning Applied to Human Visual Classification Arnulf B. A. Graf and Felix A. Wichmann	905
	Sensory Modality Segregation Virginia de Sa	913
	Reasoning about Time and Knowledge in Neural Symbolic Learning Systems Artur S. d'Avila Garcez and Luis C. Lamb	921
	Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System Marc Toussaint	929
	An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science Woojae Kim, Daniel J. Navarro, Mark A. Pitt and In Jae Myung	937
	Perception of the Structure of the Physical World Using Unknown Multimodal Sensors and Effectors D. Philipona, J. Kevin O'Regan, J.-P. Nadal and Olivier Coenen	945
	From Algorithmic to Subjective Randomness Thomas L. Griffiths and Joshua B. Tenenbaum	953
商品描述(中文翻譯) 描述： 每年一次的神經信息處理（NIPS）會議是神經計算的旗艦會議。它吸引了一個多樣化的參與者群體，包括物理學家、神經科學家、數學家、統計學家和計算機科學家。演講內容跨學科，涉及算法、學習理論、認知科學、神經科學、腦部成像、視覺、語音和信號處理、強化學習和控制、新興技術和應用等領域。只有百分之三十的論文提交被接受在NIPS上發表，因此其質量非常高。本卷收錄了2003年會議上的所有論文。 Sebastian Thrun是斯坦福大學計算機科學系的副教授，也是斯坦福人工智能實驗室的主任。 Lawrence K. Saul是賓夕法尼亞大學計算機和信息科學系的助理教授，也是2004年NIPS會議的主席。 Bernhard Schölkopf是德國圖賓根的馬克斯·普朗克生物控制研究所的主任，也是柏林工業大學的教授。 目錄： 前言 NIPS委員會 評審人員 第一部分：算法和架構 高效多尺度從高斯混合物中抽樣 Alexander T. Ihler, Erik B. Sudderth, William T. Freeman和Alan S. Willsky 第一章 類似商品 $1,800 $1,764 Advances in Neural Information Processing Systems 17 : Proceedings of the 2004 Conference (Hardcover) VIP 95折 $3,470 $3,297 Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference (Hardcover) VIP 95折 $880 $836 Semi-Supervised Learning(Hardcover) VIP 95折 $1,540 $1,463 Google's PageRank and Beyond: The Science of Search Engine Rankings 95折 $2,708 Information Theory, Inference & Learning Algorithms (Hardcover) VIP 95折 $1,590 $1,511 Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering (Synthesis Lectures on Signal Processing) VIP 95折 $2,300 $2,185 Gaussian Processes for Machine Learning (Hardcover) $1,150 $1,127 Elementary Linear Algebra, 7/e (IE-Paperback) $1,850 $1,813 Handbook of Linear Algebra (Hardcover)(書況較舊,書側有些許黴斑) VIP 95折 $2,100 $1,995 The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python