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 /
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 / [electronic resource] :
edited by David Helmbold, Bob Williamson.
- 1st ed. 2001.
- DCXLVIII, 638 p. online resource.
- Lecture Notes in Artificial Intelligence, 2111 2945-9141 ; .
- Lecture Notes in Artificial Intelligence, 2111 .
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 -Valued and Real-Valued Hypotheses -- When Can Two Unsupervised Learners Achieve PAC Separation? -- Strong Entropy Concentration, Game Theory, and Algorithmic Randomness -- Pattern Recognition and Density Estimation under the General i.i.d. Assumption -- A General Dimension for Exact Learning -- Data-Dependent Margin-Based Generalization Bounds for Classification -- Limitations of Learning via Embeddings in Euclidean Half-Spaces -- Estimating the OptimalMargins of Embeddings in Euclidean Half Spaces -- A Generalized Representer Theorem -- A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning -- Learning Additive Models Online with Fast Evaluating Kernels -- Geometric Bounds for Generalization in Boosting -- Smooth Boosting and Learning with Malicious Noise -- On Boosting with Optimal Poly-Bounded Distributions -- Agnostic Boosting -- A Theoretical Analysis of Query Selection for Collaborative Filtering -- On Using Extended Statistical Queries to Avoid Membership Queries -- Learning Monotone DNF from a Teacher That Almost Does Not Answer Membership Queries -- On Learning Monotone DNF under Product Distributions -- Learning Regular Sets with an Incomplete Membership Oracle -- Learning Rates for Q-Learning -- Optimizing Average Reward Using Discounted Rewards -- Bounds on Sample Size for Policy Evaluation in Markov Environments.
9783540445814
10.1007/3-540-44581-1 doi
Artificial intelligence.
Machine theory.
Computer science.
Algorithms.
Artificial Intelligence.
Formal Languages and Automata Theory.
Theory of Computation.
Algorithms.
Q334-342 TA347.A78
006.3
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 -Valued and Real-Valued Hypotheses -- When Can Two Unsupervised Learners Achieve PAC Separation? -- Strong Entropy Concentration, Game Theory, and Algorithmic Randomness -- Pattern Recognition and Density Estimation under the General i.i.d. Assumption -- A General Dimension for Exact Learning -- Data-Dependent Margin-Based Generalization Bounds for Classification -- Limitations of Learning via Embeddings in Euclidean Half-Spaces -- Estimating the OptimalMargins of Embeddings in Euclidean Half Spaces -- A Generalized Representer Theorem -- A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning -- Learning Additive Models Online with Fast Evaluating Kernels -- Geometric Bounds for Generalization in Boosting -- Smooth Boosting and Learning with Malicious Noise -- On Boosting with Optimal Poly-Bounded Distributions -- Agnostic Boosting -- A Theoretical Analysis of Query Selection for Collaborative Filtering -- On Using Extended Statistical Queries to Avoid Membership Queries -- Learning Monotone DNF from a Teacher That Almost Does Not Answer Membership Queries -- On Learning Monotone DNF under Product Distributions -- Learning Regular Sets with an Incomplete Membership Oracle -- Learning Rates for Q-Learning -- Optimizing Average Reward Using Discounted Rewards -- Bounds on Sample Size for Policy Evaluation in Markov Environments.
9783540445814
10.1007/3-540-44581-1 doi
Artificial intelligence.
Machine theory.
Computer science.
Algorithms.
Artificial Intelligence.
Formal Languages and Automata Theory.
Theory of Computation.
Algorithms.
Q334-342 TA347.A78
006.3