Computational Learning Theory
Language: English
Published by Springer, Springer Jul 2001, 2001
- Softcover
- New
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
AbeBooks seller since January 23, 2017
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This item is printed on demand - Print on Demand Titel. Neuware -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 .- 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 648 pp. Englisch.…
Seller Inventory # 9783540423430
- Title
- Computational Learning Theory
- Author
- David Helmbold
- Publisher
- Springer, Springer Jul 2001
- Publication year
- 2001
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3540423435
- ISBN 13
- 9783540423430
- Item weight
- 966 grams
- Dimensions
- 235x155x35 mm
"Synopsis" may belong to another edition of this title.
Synopsis
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buchversandmimpf2000
Emtmannsberg, BAYE, Germany
AbeBooks seller since January 23, 2017
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