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Computational learning theory : 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, the Netherlands, July 16-19, 2001 : proceedings

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Computational learning theory : 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, the Netherlands, July 16-19, 2001 : proceedings
Original Title Computational learning theory : 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, the Netherlands, July 16-19, 2001 : proceedings
Author Conference on Computational Learning Theory (14th : 2001 : Amsterdam, Netherlands), Helmbold, David, Williamson, Bob, 1962-, European Conference on Computational Learning Theory (5th : 2001 : Amsterdam, Netherlands)
Publication date

Topics Computational learning theory, Kunstmatige intelligentie, Leertheorieën, Apprentissage informatique, Théorie de l’, Maschinelles Lernen, Kunstmatige intelligentie, Leertheorieën, Maschinelles Lernen, Maschinelles Lernen
Publisher Berlin , New York : Springer
Collection folkscanomy_miscellaneous, folkscanomy, additional_collections
Language English
Book Type EBook
Material Type Book
File Type PDF
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Scan Quality: Best No watermark
PDF Quality: Good
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Computational Learning Theory: 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001 Amsterdam, The Netherlands, July 16–19, 2001 Proceedings
Author: David Helmbold, Bob Williamson
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-42343-0
DOI: 10.1007/3-540-44581-1

Table of Contents:

  • How Many Queries Are Needed to Learn One Bit of Information?
  • Radial Basis Function Neural Networks Have Superlinear VC Dimension
  • Tracking a Small Set of Experts by Mixing Past Posteriors
  • Potential-Based Algorithms in Online Prediction and Game Theory
  • A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applications in Learning
  • Efficiently Approximating Weighted Sums with Exponentially Many Terms
  • Ultraconservative Online Algorithms for Multiclass Problems
  • Estimating a Boolean Perceptron from Its Average Satisfying Assignment: A Bound on the Precision Required
  • Adaptive Strategies and Regret Minimization in Arbitrarily Varying Markov Environments
  • Robust Learning — Rich and Poor
  • On the Synthesis of Strategies Identifying Recursive Functions
  • Intrinsic Complexity of Learning Geometrical Concepts from Positive Data
  • Toward a Computational Theory of Data Acquisition and Truthing
  • Discrete Prediction Games with Arbitrary Feedback and Loss (Extended Abstract)
  • Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
  • Further Explanation of the Effectiveness of Voting Methods: The Game between Margins and Weights
  • Geometric Methods in the Analysis of Glivenko-Cantelli Classes
  • Learning Relatively Small Classes
  • On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses
  • When Can Two Unsupervised Learners Achieve PAC Separation?

Includes bibliographical references and index

How Many Queries Are Needed to Learn One Bit of Information? / Hans Ulrich Simon — Radial Basis Function Neural Networks Have Superlinear VC Dimension / Michael Schmitt — Tracking a Small Set of Experts by Mixing Past Posteriors / Oliver Bousquet and Manfred K. Warmuth — Potential-Based Algorithms in On-Line Prediction and Game Theory / Nicolo Cesa-Bianchi and Gabor Lugosi — A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applications in Learning / Tong Zhang — Efficiently Approximating Weighted Sums with Exponentially Many Terms / Deepak Chawla, Lin Li and Stephen Scott — Ultraconservative Online Algorithms for Multiclass Problems / Koby Crammer and Yoram Singer — Estimating a Boolean Perceptron from Its Average Satisfying Assignment: A Bound on the Precision Required / Paul W. Goldberg — Adaptive Strategies and Regret Minimization in Arbitrarily Varying Markov Environments / Shie Mannor and Nahum Shimkin — Robust Learning — Rich and Poor / John Case, Sanjay Jain and Frank Stephan / [et al.] — On the Synthesis of Strategies Identifying Recursive Functions / Sandra Zilles — Intrinsic Complexity of Learning Geometrical Concepts from Positive Data / Sanjay Jain and Efim Kinber — Toward a Computational Theory of Data Acquisition and Truthing / David G. Stork — Discrete Prediction Games with Arbitrary Feedback and Loss / Antonio Piccolboni and Christian Schindelhauer — Rademacher and Gaussian Complexities: Risk Bounds and Structural Results / Peter L. Bartlett and Shahar Mendelson — Further Explanation of the Effectiveness of Voting Methods: The Game between Margins and Weights / Vladimir Koltchinskii, Dmitriy Panchenko and Fernando Lozano — Geometric Methods in the Analysis of Glivenko-Cantelli Classes / Shahar Mendelson — Learning Relatively Small Classes / Shahar Mendelson — On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses / Philip M. Long — When Can Two Unsupervised Learners Achieve PAC Separation? / Paul W. Goldberg — Strong Entropy Concentration, Game Theory and Algorithmic Randomness / Peter Grunwald — Pattern Recognition and Density Estimation under the General i.i.d. Assumption / Ilia Nouretdinov, Volodya Vovk and Michael Vyugin / [et al.] — A General Dimension for Exact Learning / Jose L. Balcazar, Jorge Castro and David Guijarro — Data-Dependent Margin-Based Generalization Bounds for Classification / Balazs Kegl, Tamas Linder and Gabor Lugosi — Limitations of Learning Via Embeddings in Euclidean Half-Spaces / Shai Ben-David, Nadav Eiron and Hans Ulrich Simon — Estimating the Optimal Margins of Embeddings in Euclidean Half Spaces / Jurgen Forster, Niels Schmitt and Hans Ulrich Simon — A Generalized Representer Theorem / Bernhard Scholkopf, Ralf Herbrich and Alex J. Smola — A Leave-One Out Cross Validation Bound for Kernel Methods with Applications in Learning / Tong Zhang — Learning Additive Models Online with Fast Evaluating Kernels / Mark Herbster — Geometric Bounds for Generalization in Boosting / Shie Mannor and Ron Meir — Smooth Boosting and Learning with Malicious Noise / Rocco A. Servedio — On Boosting with Optimal Poly-Bounded Distributions / Nader H. Bshouty and Dmitry Gavinsky — Agnostic Boosting / Shai Ben-David, Philip M. Long and Yishay Mansour — A Theoretical Analysis of Query Selection for Collaborative Filtering / Wee Sun Lee and Philip M. Long — On Using Extended Statistical Queries to Avoid Membership Queries / Nader H. Bshouty and Vitaly Feldman — Learning Monotone DNF from a Teacher That Almost Does Not Answer Membership Queries / Nader H. Bshouty and Nadav Eiron — On Learning Montone DNF under Product Distributions / Rocco A. Servedio — Learning Regular Sets with an Incomplete Membership Oracle / Nader Bshouty and Avi Owshanko — Learning Rates for Q-Learning / Eyal Even-Dar and Yishay Mansour — Optimizing Average Reward Using Discounted Rewards / Sham Kakade — Bounds on Sample Size for Policy Evaluation in Markov Environments / Leonid Peshkin and Sayan Mukherjee

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