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))
Sebastian Thrun, Lawrence K. Saul, Bernhard Schlkopf
- 出版商: MIT
- 出版日期: 2004-06-04
- 售價: $1,800
- 語言: 英文
- 頁數: 1728
- 裝訂: Hardcover
- ISBN: 0262201526
- ISBN-13: 9780262201520
-
相關分類:
人工智慧、DeepLearning
立即出貨(限量) (庫存=5)
相關主題
商品描述
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. Willsky1 Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles
Mark Girolami and Ata Kaban9 Hierarchical Topic Models and the Nested Chinese Restaurant Process
David M. Blei, Thomas L. Griffiths, Joshua B. Tenenbaum and Michael I. Jordan17 Max-Margin Markov Networks
Benjamin Taskar, Carlos Guestrin and Daphne Koller25 Invariant Pattern Recognition by Semi-Definite Programming Machines
Thore Graepel and Ralf Herbrich33 Learning a Distance Metric from Relative Comparisons
Matthew Schultz and Thorsten Joachims41 1-norm Support Vector Machines
Ji Zhu, Saharon Rosset, Trevor Hastie and Robert Tibshirani49 Image Reconstruction by Linear Programming
Koji Tsuda and Gunnar Rätsch57 Multiple-Instance Learning via Disjunctive Programming Boosting
Stuart Andrews and Thomas Hofmann65 Convex Methods for Transduction
Tijl De Bie and Nello Cristianini73 Kernel Dimensionality Reduction for Supervised Learning
Kenji Fukumizu, Francis R. Bach and Michael I. Jordan81 Clustering with the Connectivity Kernel
Bernd Fischer, Volker Roth and Joachim M. Buhmann89 Efficient and Robust Feature Extraction by Maximum Margin Criterion
Haifeng Li, Tao Jiang and Keshu Zhang97 Sparse Greedy Minimax Probability Machine Classification
Thomas Strohmann, Andrei Belitski, Greg Grudic and Dennis DeCoste105 Sequential Bayesian Kernel Regression
Jaco Vermaak, Simon J. Godsill and Arnaud Doucet113 Fast Feature Selection from Microarray Expression Data via Multiplicative Large Margin Algorithms
Claudio Gentile121 Dynamical Modeling with Kernels for Nonlinear Time Series Prediction
Liva Ralaivola and Florence d'Alché-Buc129 Extreme Components Analysis
Max Welling, Felix Agakov and Christopher K. I. Williams137 Linear Dependent Dimensionality Reduction
Nathan Srebro and Tommi Jaakkola145 Locality Preserving Projections
Xiaofei He and Partha Niyogi153 Optimal Manifold Representation of Data: An Information Theoretic Approach
Denis V. Chigirev and William Bialek161 Ranking on Data Manifolds
Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet and Bernhard Schölkopf169 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 Ouimet177 Pairwise Clustering and Graphical Models
Noam Shental, Assaf Zomet, Tomer Hertz and Yair Weiss185 Tree-structured Approximations by Expectation Propagation
Thomas Minka and Yuan Qi193 The IM Algorithm: A Variational Approach to Information Maximization
David Barber and Felix Agakov201 Iterative Scaled Trust-Region Learning in Krylov Subspaces via Pearlmutter's Implicit Sparse Hessian-Vector Multiply
Eiji Mizutani and James W. Demmel209 Large Scale Online Learning
Léon Bottou and Yann Le Cun217 Online Classification on a Budget
Koby Crammer, Jaz Kandola and Yoram Singer225 Online Learning via Global Feedback for Phrase Recognition
Xavier Carreras and Lluis Marquez233 Sparse Representation and Its Applications in Blind Source Separation
Yuanqing Li, Andrzej Cichocki, Shun-ichi Amari, Sergei Shishkin, Jianting Cao and Fanji Gu241 Perspectives on Sparse Bayesian Learning
David Wipf, Jason Palmer and Bhaskar D. Rao249 Semi-Supervised Learning with Trees
Charles Kemp, Thomas L. Griffiths, Sean Stromsten and Joshua B. Tenenbaum257 Efficient Exact k-NN and Nonparametric Classification in High Dimensions
Ting Liu, Andrew W. Moore and Alexander Gray265 Nonstationary Covariance Functions for Gaussian Process Regression
Christopher J. Paciorek and Mark J. Schervish273 Learning the k in k-means
Greg Hamerly and Charles Elkan281 Finding the M Most Probable Configurations in Arbitrary Graphical Models
Chen Yanover and Yair Weiss289 Non-linear CCA and PCA by Alignment of Local Models
Jakob J. Verbeek, Sam T. Roweis and Nikos Vlassis297 Learning Spectral Clustering
Francis R. Bach and Michael I. Jordan305 AUC Optimization vs. Error Rate Minimization
Corinna Cortes and Mehryar Mohri313 Learning with Local and Global Consistency
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston and Bernhard Schölkopf321 Gaussian Process Latent Variable Models for Visualization of High Dimensional Data
Neil D. Lawrence329 Warped Gaussian Processes
Edward Snelson, Carl Edward Rasmussen and Zoubin Ghahramani337 Can We Learn to Beat the Best Stock
Allan Borodin, Ran El-Yaniv and Vincent Gogan345 Approximate Expectation Maximization
Tom Heskes, Onno Zoeter and Wim Wiegerinck353 Linear Response for Approximate Inference
Max Welling and Yee Whye Teh361 Semidefinite Relaxations for Approximate Inference on Graphs with Cycles
Martin Wainwright and Michael I. Jordan369 Approximability of Probability Distributions
Alina Beygelzimer and Irina Rish377 Denoising and Untangling Graphs Using Degree Priors
Quaid D. Morris and Brendan J. Frey385 On the Concentration of Expectation and Approximate Inference in Layered Networks
XuanLong Nguyen and Michael I. Jordan393 Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models
Radford M. Neal, Matthew J. Beal and Sam T. Roweis401 Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis
Pedro F. Felzenszwalb, Daniel P. Huttenlocher and Jon M. Kleinberg409 Wormholes Improve Contrastive Divergence
Geoffrey Hinton, Max Welling and Andriy Mnih417 Sample Propagation
Mark A. Paskin425 Generalised Propagation for Fast Fourier Transforms with Partial or Missing Data
Amos J. Storkey433 Laplace Propagation
Alexander Smola, Vishy Vishwanathan and Eleazar Eskin441 Learning to Find Pre-Images
Geokhan H. Bakir, Jason Weston and Bernhard Schölkopf449 Semi-Definite Programming by Perceptron Learning
Thore Graepel, Ralf Herbrich, Andriy Kharechko and John Shawe-Taylor457 Computing Gaussian Mixture Models with EM Using Equivalence Constraints
Noam Shental, Aharon Bar-Hillel, Tomer Hertz and Daphna Weinshall465 Feature Selection in Clustering Problems
Volker Roth and Tilman Lange473 An Iterative Improvement Procedure for Hierarchical Clustering
David Kauchak and Sanjoy Dasgupta481 Identifying Structure across Pre-Partitioned Data
Zvika Marx, Ido Dagan and Eli Shamir489 Log-Linear Models for Label Ranking
Ofer Dekel, Christopher Manning and Yoram Singer497 Minimax Embeddings
Matthew Brand505 No Unbiased Estimator of the Variance of K-Fold Cross-Validation
Yoshua Bengio and Yves Grandvalet513 Bias-Corrected Bootstrap and Model Uncertainty
Harald Steck and Tommi Jaakkola521 Probability Estimates for Multi-Class Classification by Pairwise Coupling
Ting-Fan Wu, Chih-Jen Lin and Ruby C. Weng529 Necessary Intransitive Likelihood-Ratio Classifiers
Gang Ji and Jeff Bilmes537 Classification with Hybrid Generative/Discriminative Models
Rajat Raina, Yirong Shen, Andrew Y. Ng and Andrew McCallum545 A Model for Learning the Semantics of Pictures
Victor Lavrenko, R. Manmatha and Jiwoon Jeon553 Algorithms for Interdependent Security Games
Michael Kearns and Luis Ortiz561 II Applications Fast Embedding of Sparse Similarity Graphs
John C. Platt571 GPPS: A Gaussian Process Positioning System for Cellular Networks
Anton Schwaighofer, Marian Grigoras, Volker Tresp and Clemens Hoffmann579 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 Thrun587 Semi-supervised Protein Classification Using Cluster Kernels
Jason Weston, Christina Leslie, Dengyong Zhou, André Elisseeff and William S. Noble595 Statistical Debugging of Sampled Programs
Alice X. Zheng, Michael I. Jordan, Ben Liblit and Alex Aiken603 Markov Models for Automated ECG Interval Analysis
Nicholas P. Hughes, Lionel Tarassenko and Stephen Roberts611 Parameterized Novelty Detectors for Environmental Sensor Monitoring
Cynthia Archer, Todd K. Leen and Antonio Baptista619 Modeling User Rating Profiles for Collaborative Filtering
Benjamin Marlin627 Application of SVMs for Colour Classification and Collision Detection with AIBO Robots
Michael J. Quinlan, Stephan K. Chalup and Richard H. Middleton635 Kernels for Structured Natural Language Data
Jun Suzuki, Yutaka Sasaki and Eisaku Maeda643 A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters
Daniel B. Neill and Andrew W. Moore651 Link Prediction in Relational Data
Benjamin Taskar, Ming-Fai Wong, Pieter Abbeel and Daphne Koller659 Unsupervised Color Decomposition of Histologically Stained Tissue Samples
Andrew Rabinovich, Sameer Agarwal, Casey Laris, Jeffrey H. Price and Serge J. Belongie667 ICA-based Clustering of Genes from Microarray Expression Data
Su-In Lee and Serafim Batzoglou675 Gene Expression Clustering with Functional Mixture Models
Darya Chudova, Christopher Hart, Eric Mjolsness and Padhraic Smyth683 III Brain Imaging Reconstructing MEG Sources with Unknown Correlations
Maneesh Sahani and Srikantan Nagarajan693 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 Yamawaki701 Training fMRI Classifiers to Discriminate Cognitive States across Multiple Subjects
Xuerui Wang, Rebecca Hutchinson and Tom M. Mitchell709 Nonlinear Filtering of Electron Micrographs by Means of Support Vector Regression
Roland Vollgraf, Michael Scholz, Ian A. Meinertzhagen and Klaus Obermayer717 Impact of an Energy Normalization Transform on the Performance of the LF-ASD Brain Computer Interface
Yu Zhou, Steven G. Mason and Gary E. Birch725 Increase Information Transfer Rates in BCI by CSP Extension to Multi-class
Guido Dornhege, Benjamin Blankertz, Gabriel Curio and Klaus-Robert Müller733 Subject-Independent Magnetoencephalographic Source Localization by a Multilayer Perceptron
Sung C. Jun and Barak A. Pearlmutter741 IV Control and Reinforcement Learning Gaussian Processes in Reinforcement Learning
Carl Edward Rasmussen and Malte Kuss751 Applying Metric-Trees to Belief-Point POMDPs
Jolette Pineau, Geoffrey J. Gordon and Sebastian Thrun759 ARA*: Anytime A* with Provable Bounds on Sub-Optimality
Maxim Likhachev, Geoffrey J. Gordon and Sebastian Thrun767 Approximate Planning in POMDPs with Macro-Actions
Georgios Theocharous and Leslie Pack Kaelbling775 Envelope-based Planning in Relational MDPs
Natalia H. Gardiol and Leslie Pack Kaelbling783 An MDP-Based Approach to Online Mechanism Design
David C. Parkes and Satinder P. Singh791 Autonomous Helicopter Flight via Reinforcement Learning
Andrew Y. Ng, H. Jin Kim, Michael I. Jordan and Shankar Sastry799 All learning is Local: Multi-agent Learning in Global Reward Games
Yu-Han Chang, Tracey Ho and Leslie Pack Kaelbling807 How to Combine Expert (and Novice) Advice when Actions Impact the Environment?
Daniela Pucci de Farias and Nimrod Megiddo815 Bounded Finite State Controllers
Pascal Poupart and Craig Boutilier823 Policy Search by Dynamic Programming
J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng and Jeff Schneider831 Robustness in Markov Decision Problems with Uncertain Transition Matrices
Arnab Nilim and Laurent El Ghaoui839 Approximate Policy Iteration with a Policy Language Bias
Alan Fern, Sungwook Yoon and Robert Givan847 A Nonlinear Predictive State Representation
Matthew R. Rudary and Satinder P. Singh855 Learning Near-Pareto-Optimal Conventions in Polynomial Time
XiaoFeng Wang and Tuomas Sandholm863 Extending Q-Learning to General Adaptive Multi-Agent Systems
Gerald Tesauro871 Auction Mechanism Design for Multi-Robot Coordination
Curt Bererton, Geoffrey J. Gordon and Sebastian Thrun879 Distributed Optimization in Adaptive Networks
Ciamac C. Moallemi and Benjamin Van Roy887 Linear Program Approximations for Factored Continuous-State Markov Decision Processes
Milos Hauskrecht and Branislav Kveton895 V Cognitive Science and Artificial Intelligence Insights from Machine Learning Applied to Human Visual Classification
Arnulf B. A. Graf and Felix A. Wichmann905 Sensory Modality Segregation
Virginia de Sa913 Reasoning about Time and Knowledge in Neural Symbolic Learning Systems
Artur S. d'Avila Garcez and Luis C. Lamb921 Learning a World Model and Planning with a Self-Organizing, Dynamic Neural System
Marc Toussaint929 An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science
Woojae Kim, Daniel J. Navarro, Mark A. Pitt and In Jae Myung937 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 Coenen945 From Algorithmic to Subjective Randomness
Thomas L. Griffiths and Joshua B. Tenenbaum953 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
Advances in Neural Information Processing Systems 17 : Proceedings of the 2004 Conference (Hardcover)$1,800$1,764- VIP 95折
Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference (Hardcover)$3,470$3,297- VIP 95折
Semi-Supervised Learning(Hardcover)$880$836- VIP 95折
Google's PageRank and Beyond: The Science of Search Engine Rankings$1,540$1,463- 95折
$2,708Information Theory, Inference & Learning Algorithms (Hardcover)- VIP 95折
Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering (Synthesis Lectures on Signal Processing)$1,590$1,511- VIP 95折
Gaussian Processes for Machine Learning (Hardcover)$2,300$2,185 Elementary Linear Algebra, 7/e (IE-Paperback)$1,150$1,127 Handbook of Linear Algebra (Hardcover)(書況較舊,書側有些許黴斑)$1,850$1,813- VIP 95折
The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python$2,100$1,995