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Bob Williamson:

Computational Learning Theory - Taschenbuch

2004, ISBN: 9783540423430

[ED: Taschenbuch], [PU: Springer Berlin Heidelberg], Neuware - This volume contains papers presented at the joint 14th Annual Conference on Computational Learning Theory and 5th European … Mehr…

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Bob Williamson:

Computational Learning Theory - Taschenbuch

2004, ISBN: 9783540423430

[ED: Taschenbuch], [PU: Springer Berlin Heidelberg], Neuware - This volume contains papers presented at the joint 14th Annual Conference on Computational Learning Theory and 5th European … Mehr…

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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 - Williamson, Bob (Herausgeber); Helmbold, David (Herausgeber)
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Williamson, Bob (Herausgeber); Helmbold, David (Herausgeber):
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 - neues Buch

2001

ISBN: 3540423435

2001 Kartoniert / Broschiert Data Mining (EDV), EDV / Theorie / Software-Entw. / Software-Design, Intelligenz / Künstliche Intelligenz, KI, Künstliche Intelligenz - AI, Wissensbasiertes… Mehr…

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Computational Learning Theory - Taschenbuch

2001, ISBN: 9783540423430

*Computational Learning Theory* - 14th Annual Conference on Computational Learning Theory COLT 2001 and 5th European Conference on Computational Learning Theory EuroCOLT 2001 Amsterdam Th… Mehr…

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David Helmbold; Bob Williamson:
Computational Learning Theory - Taschenbuch

2001, ISBN: 9783540423430

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, 200… Mehr…

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Bibliographische Daten des bestpassenden Buches

Details zum Buch
Computational Learning Theory
Autor:
Titel:
ISBN-Nummer:

9783540423430


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.

Detailangaben zum Buch - Computational Learning Theory


EAN (ISBN-13): 9783540423430
ISBN (ISBN-10): 3540423435
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2001
Herausgeber: Springer Berlin Heidelberg
648 Seiten
Gewicht: 0,964 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2007-06-20T19:43:18+02:00 (Zurich)
Detailseite zuletzt geändert am 2024-02-09T16:24:26+01:00 (Zurich)
ISBN/EAN: 9783540423430

ISBN - alternative Schreibweisen:
3-540-42343-5, 978-3-540-42343-0
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: helmbold, theory, bob williams, williamson, david helm, july, colt
Titel des Buches: colt, european conference artificial intelligence, learning learning, proceedings artificial intelligence conference, theory, lecture notes artificial intelligence, amsterdam, 2001, the

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: 2001-07-04
Berlin; Heidelberg; DE
Sprache: Englisch
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
Available
DCXLVIII, 638 p.

BC; Hardcover, Softcover / 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; Artificial Intelligence; Formal Languages and Automata Theory; Theory of Computation; Algorithms; Theoretische Informatik; Algorithmen und Datenstrukturen; EA

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
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