Computational learning theory : 14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th Eur
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)
Added by: sketch
Added Date: 2015-12-30
Language: eng
Subjects: Computational learning theory, Kunstmatige intelligentie, Leertheorieën, Apprentissage informatique, Théorie de l', Maschinelles Lernen, Kunstmatige intelligentie, Leertheorieën, Maschinelles Lernen, Maschinelles Lernen
Publishers: Berlin ; New York : Springer
Collections: journals contributions, journals
ISBN Number: 3540423435, 9783540423430
Pages Count: 300
PPI Count: 300
PDF Count: 1
Total Size: 292.98 MB
PDF Size: 7.59 MB
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Edition: [Elektronische Ressource]
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Last Modified: 2023-05-26 03:10:47
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Description
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