This book constitutes the refereed proceedings of the 14th Annual and 5th European Conferences on Computational Learning Theory, COLT/EuroCOLT 2001, held in Amsterdam, The Netherlands, in July 2001.
The 40 revised full papers presented together with one invited paper were carefully reviewed and selected from a total of 69 submissions. All current aspects of computational learning and its applications in a variety of fields are addressed.
Author(s): Hans Ulrich Simon (auth.), David Helmbold, Bob Williamson (eds.)
Series: Lecture Notes in Computer Science 2111 : Lecture Notes in Artificial Intelligence
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2001
Language: English
Pages: 638
Tags: Artificial Intelligence (incl. Robotics); Mathematical Logic and Formal Languages; Computation by Abstract Devices; Algorithm Analysis and Problem Complexity
How Many Queries Are Needed to Learn One Bit of Information?....Pages 1-13
Radial Basis Function Neural Networks Have Superlinear VC Dimension....Pages 14-30
Tracking a Small Set of Experts by Mixing Past Posteriors....Pages 31-47
Potential-Based Algorithms in Online Prediction and Game Theory....Pages 48-64
A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applications in Learning....Pages 65-81
Efficiently Approximating Weighted Sums with Exponentially Many Terms....Pages 82-98
Ultraconservative Online Algorithms for Multiclass Problems....Pages 99-115
Estimating a Boolean Perceptron from Its Average Satisfying Assignment: A Bound on the Precision Required....Pages 116-127
Adaptive Strategies and Regret Minimization in Arbitrarily Varying Markov Environments....Pages 128-142
Robust Learning — Rich and Poor....Pages 143-159
On the Synthesis of Strategies Identifying Recursive Functions....Pages 160-176
Intrinsic Complexity of Learning Geometrical Concepts from Positive Data....Pages 177-193
Toward a Computational Theory of Data Acquisition and Truthing....Pages 194-207
Discrete Prediction Games with Arbitrary Feedback and Loss (Extended Abstract)....Pages 208-223
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results....Pages 224-240
Further Explanation of the Effectiveness of Voting Methods: The Game between Margins and Weights....Pages 241-255
Geometric Methods in the Analysis of Glivenko-Cantelli Classes....Pages 256-272
Learning Relatively Small Classes....Pages 273-288
On Agnostic Learning with {0, *, 1}-Valued and Real-Valued Hypotheses....Pages 289-302
When Can Two Unsupervised Learners Achieve PAC Separation?....Pages 303-319
Strong Entropy Concentration, Game Theory, and Algorithmic Randomness....Pages 320-336
Pattern Recognition and Density Estimation under the General i.i.d. Assumption....Pages 337-353
A General Dimension for Exact Learning....Pages 354-367
Data-Dependent Margin-Based Generalization Bounds for Classification....Pages 368-384
Limitations of Learning via Embeddings in Euclidean Half-Spaces....Pages 385-401
Estimating the Optimal Margins of Embeddings in Euclidean Half Spaces....Pages 402-415
A Generalized Representer Theorem....Pages 416-426
A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning....Pages 427-443
Learning Additive Models Online with Fast Evaluating Kernels....Pages 444-460
Geometric Bounds for Generalization in Boosting....Pages 461-472
Smooth Boosting and Learning with Malicious Noise....Pages 473-489
On Boosting with Optimal Poly-Bounded Distributions....Pages 490-506
Agnostic Boosting....Pages 507-516
A Theoretical Analysis of Query Selection for Collaborative Filtering....Pages 517-528
On Using Extended Statistical Queries to Avoid Membership Queries....Pages 529-545
Learning Monotone DNF from a Teacher That Almost Does Not Answer Membership Queries....Pages 546-557
On Learning Monotone DNF under Product Distributions....Pages 558-573
Learning Regular Sets with an Incomplete Membership Oracle....Pages 574-588
Learning Rates for Q-Learning....Pages 589-604
Optimizing Average Reward Using Discounted Rewards....Pages 605-615
Bounds on Sample Size for Policy Evaluation in Markov Environments....Pages 616-629