Algorithmic Learning Theory, Sanjay Jain; R?mi Munos; Frank Stephan; Thomas Zeu

Автор: Ricard Gavalda; Gabor Lugosi; Thomas Zeugmann; San
Название: Algorithmic Learning Theory
ISBN: 3642044131 ISBN-13(EAN): 9783642044137
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Описание: The papers are divided into topical sections of papers on online learning, learning graphs, active learning and query learning, statistical learning, inductive inference, and semisupervised and unsupervised learning.

Автор: Hiroki Arimura; Sanjay Jain; Arun Sharma
Название: Algorithmic Learning Theory
ISBN: 3540412379 ISBN-13(EAN): 9783540412373
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Описание: These papers on algorithmic learning theory are organized in topical sections on statistical learning, inductive logic programming, inductive inference, complexity, neural networks and other paradigms, support vector machines.

Автор: Ortner
Название: Algorithmic Learning Theory
ISBN: 3319463780 ISBN-13(EAN): 9783319463780
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Цена: 54040.00 T
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Описание: This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. statistical learning, theory, evolvability; exact and interactive learning;

Автор: Kamalika Chaudhuri; CLAUDIO GENTILE; Sandra Zilles
Название: Algorithmic Learning Theory
ISBN: 331924485X ISBN-13(EAN): 9783319244853
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Описание: This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015.

Автор: Nicol? Cesa-Bianchi; Masayuki Numao; R?diger Reisc
Название: Algorithmic Learning Theory
ISBN: 3540001700 ISBN-13(EAN): 9783540001706
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Описание: Compiled from the proceedings of the 13th International Conference on Algorithmic Learning Theory, this volume contains 31 papers. Coverage includes Boolean functions, boosting and margin-based learning, learning with queries, learning and information extraction, and inductive inference.

Автор: Naoki Abe; Roni Khardon; Thomas Zeugmann
Название: Algorithmic Learning Theory
ISBN: 3540428755 ISBN-13(EAN): 9783540428756
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Описание: This volume contains the papers presented at the 12th Annual Conference on Algorithmic Learning Theory (ALT 2001), which was held in Washington DC, USA, during November 25-28, 2001. Cohen of the University of Massachusetts at Amherst, USA, and the joint invited talk for ALT 2001 and DS 2001 presented by Setsuo Arikawa of Kyushu University, Japan.

Автор: Setsuo Arikawa; Klaus P. Jantke
Название: Algorithmic Learning Theory
ISBN: 3540585206 ISBN-13(EAN): 9783540585206
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Цена: 93160.00 T
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Описание: This volume contains a number of research papers on current aspects of computational learning theory. The contributors focus on algorithmic learning, machine learning, analogical inference, inductive logic, case-based reasoning and formal language learning.

Автор: Michael M. Richter; Carl H. Smith; Rolf Wiehagen;
Название: Algorithmic Learning Theory
ISBN: 354065013X ISBN-13(EAN): 9783540650133
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Описание: A collection of papers from the 9th International Conference on Algorithmic Learning Theory. They are organized in sections on inductive logic programming and data mining, inductive inference, learning via queries, prediction algorithms, inductive logic programming and learning formal languages.

Автор: Marios Mavronicolas; Maria Papadoupoulou
Название: Algorithmic Game Theory
ISBN: 3642046444 ISBN-13(EAN): 9783642046445
Издательство: Springer
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Описание: This volume contains the papers presented at the Second International Sym- sium on Algorithmic Game Theory (SAGT 2009), which was held on October 18-20, 2009, in Paphos, Cyprus. This event followed the ?rst, very successful SAGT symposium, which took place in Paderborn, Germany, last year. The purpose of SAGT is to bring together researchers from computer s- ence, economics and mathematics to present and discuss originalresearchat the intersection of algorithms and game theory. It has been intended to cover all important areas such as solution concepts, game classes, computation of equil- riaandmarketequilibria, algorithmicmechanismdesign, automatedmechanism design, convergenceandlearningingames, complexityclassesingametheory, - gorithmicaspectsof?xed-pointtheorems, mechanisms, incentivesandcoalitions, cost-sharing algorithms, computational problems in economics, ?nance, decision theory and pricing, computational social choice, auction algorithms, price of - archyand its relatives, representationsof games and their complexity, economic aspects of distributed computing and the internet, congestion, routing and n- work design and formation games and game-theoretic approaches to networking problems. Approximately55submissionstoSAGT2009 werereceived.Eachsubmission was reviewed by at least three Program Committee members. The Program Committee decided to accept 29 papers. Out of these, a small number will be invited to a Special Issue of the Theory of Computing Systems journal with selected papers from SAGT 2009. The program of SAGT 2009 featured three invited talks from three outstanding researchers in algorithmic game theory: Elias Koutsoupias, Dov Monderer and Mihalis Yannakakis. We are very grateful toElias, DovandMihalisforjoiningusinPaphosandfortheirexcellentlectures.

Автор: Shuji Doshita; Koichi Furukawa; Klaus P. Jantke; T
Название: Algorithmic Learning Theory - ALT `92
ISBN: 3540573690 ISBN-13(EAN): 9783540573692
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Описание: This volume contains the research papers presented at the 3rd Workshop on Algorithmic Learning Theory, held in Tokyo, Japan, in October 1992. It is organized into sections on neural networks, learning via query, inductive inference, analogical reasoning and approximate learning.

Автор: Klaus P. Jantke; Shigenobu Kobayashi; Etsuji Tomit
Название: Algorithmic Learning Theory
ISBN: 3540573704 ISBN-13(EAN): 9783540573708
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Описание: This volume contains all the papers that were presented at the Fourth Workshop on Algorithmic Learning Theory, held in Tokyo in November 1993. It is organized into parts on inductive logic and inference; approximate learning; query learning; explanation-based learning; and new learning paradigms.

Автор: Klaus P. Jantke; Takeshi Shinohara; Thomas Zeugman
Название: Algorithmic Learning Theory
ISBN: 3540604545 ISBN-13(EAN): 9783540604549
Издательство: Springer
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Описание: These conference proceedings cover current areas related to algorithmic learning theory, in particular the theory of machine learning, design and analysis of learning algorithms, computational logic aspects, inductive inference, learning via queries, and pattern recognition.