Computational Learning Theory - neues Buch

EAN (ISBN-13): 9783540445814
Erscheinungsjahr: 2001
Herausgeber: Springer Berlin Heidelberg

Buch in der Datenbank seit 2017-02-16T11:38:34+01:00 (Vienna)
Detailseite zuletzt geändert am 2023-09-22T15:40:01+02:00 (Vienna)
ISBN/EAN: 9783540445814

ISBN - alternative Schreibweisen:
978-3-540-44581-4
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: helmbold, williamson, helm david
Titel des Buches: learning, amsterdam, colt

---

Daten vom Verlag:

Autor/in: David Helmbold; Bob Williamson
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; 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
Verlag: Springer; Springer Berlin
638 Seiten
Erscheinungsjahr: 2003-06-29
Berlin; Heidelberg; DE
Sprache: Englisch
96,29 € (DE)
99,00 € (AT)
118,00 CHF (CH)
Available
DCXLVIII, 638 p.

EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Algorithmic Learning; Boosting; Classification; Computational Learning; Computational Learning Theory; Data Mining; Game Theory; Inference; Q-Learning; algorithms; cognition; complexity; kernel method; learning theory; optimization; algorithm analysis and problem complexity; C; Artificial Intelligence; Formal Languages and Automata Theory; Theory of Computation; Algorithms; Computer Science; Theoretische Informatik; Algorithmen und Datenstrukturen; BC

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.
Includes supplementary material: sn.pub/extras