Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Sanjay Jain
Publisher: Springer Science & Business Media
ISBN: 354029242X
Category : Computers
Languages : en
Pages : 502

Get Book Here

Book Description
This book constitutes the refereed proceedings of the 16th International Conference on Algorithmic Learning Theory, ALT 2005, held in Singapore in October 2005. The 30 revised full papers presented together with 5 invited papers and an introduction by the editors were carefully reviewed and selected from 98 submissions. The papers are organized in topical sections on kernel-based learning, bayesian and statistical models, PAC-learning, query-learning, inductive inference, language learning, learning and logic, learning from expert advice, online learning, defensive forecasting, and teaching.

Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Sanjay Jain
Publisher: Springer Science & Business Media
ISBN: 354029242X
Category : Computers
Languages : en
Pages : 502

Get Book Here

Book Description
This book constitutes the refereed proceedings of the 16th International Conference on Algorithmic Learning Theory, ALT 2005, held in Singapore in October 2005. The 30 revised full papers presented together with 5 invited papers and an introduction by the editors were carefully reviewed and selected from 98 submissions. The papers are organized in topical sections on kernel-based learning, bayesian and statistical models, PAC-learning, query-learning, inductive inference, language learning, learning and logic, learning from expert advice, online learning, defensive forecasting, and teaching.

Algorithmic Learning Theory II

Algorithmic Learning Theory II PDF Author: Setsuo Arikawa
Publisher: IOS Press
ISBN: 9784274076992
Category : Algorithms
Languages : en
Pages : 324

Get Book Here

Book Description


Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Yoav Freund
Publisher: Springer
ISBN: 3540879870
Category : Computers
Languages : en
Pages : 480

Get Book Here

Book Description
This volume contains papers presented at the 19th International Conference on Algorithmic Learning Theory (ALT 2008), which was held in Budapest, Hungary during October 13–16, 2008. The conference was co-located with the 11th - ternational Conference on Discovery Science (DS 2008). The technical program of ALT 2008 contained 31 papers selected from 46 submissions, and 5 invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2008 was the 19th in the ALT conference series, established in Japan in 1990. The series Analogical and Inductive Inference is a predecessor of this series: it was held in 1986, 1989 and 1992, co-located with ALT in 1994, and s- sequently merged with ALT. ALT maintains its strong connections to Japan, but has also been held in other countries, such as Australia, Germany, Italy, Sin- pore, Spain and the USA. The ALT conference series is supervised by its Steering Committee: Naoki Abe (IBM T. J.

Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Michael M. Richter
Publisher: Springer Science & Business Media
ISBN: 354065013X
Category : Artificial intelligence
Languages : en
Pages : 450

Get Book Here

Book Description
This volume contains all the papers presented at the Ninth International Con- rence on Algorithmic Learning Theory (ALT’98), held at the European education centre Europ ̈aisches Bildungszentrum (ebz) Otzenhausen, Germany, October 8{ 10, 1998. The Conference was sponsored by the Japanese Society for Arti cial Intelligence (JSAI) and the University of Kaiserslautern. Thirty-four papers on all aspects of algorithmic learning theory and related areas were submitted, all electronically. Twenty-six papers were accepted by the program committee based on originality, quality, and relevance to the theory of machine learning. Additionally, three invited talks presented by Akira Maruoka of Tohoku University, Arun Sharma of the University of New South Wales, and Stefan Wrobel from GMD, respectively, were featured at the conference. We would like to express our sincere gratitude to our invited speakers for sharing with us their insights on new and exciting developments in their areas of research. This conference is the ninth in a series of annual meetings established in 1990. The ALT series focuses on all areas related to algorithmic learning theory including (but not limited to): the theory of machine learning, the design and analysis of learning algorithms, computational logic of/for machine discovery, inductive inference of recursive functions and recursively enumerable languages, learning via queries, learning by arti cial and biological neural networks, pattern recognition, learning by analogy, statistical learning, Bayesian/MDL estimation, inductive logic programming, robotics, application of learning to databases, and gene analyses.

Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Jyriki Kivinen
Publisher: Springer
ISBN: 3642244122
Category : Computers
Languages : en
Pages : 465

Get Book Here

Book Description
This book constitutes the refereed proceedings of the 22nd International Conference on Algorithmic Learning Theory, ALT 2011, held in Espoo, Finland, in October 2011, co-located with the 14th International Conference on Discovery Science, DS 2011. The 28 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from numerous submissions. The papers are divided into topical sections of papers on inductive inference, regression, bandit problems, online learning, kernel and margin-based methods, intelligent agents and other learning models.

Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Kamalika Chaudhuri
Publisher: Springer
ISBN: 9783319244853
Category : Computers
Languages : en
Pages : 0

Get Book Here

Book Description
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. The 23 full papers presented in this volume were carefully reviewed and selected from 44 submissions. In addition the book contains 2 full papers summarizing the invited talks and 2 abstracts of invited talks. The papers are organized in topical sections named: inductive inference; learning from queries, teaching complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory.

Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Michael M. Richter
Publisher: Springer
ISBN: 3540497307
Category : Computers
Languages : en
Pages : 450

Get Book Here

Book Description
This volume contains all the papers presented at the Ninth International Con- rence on Algorithmic Learning Theory (ALT’98), held at the European education centre Europ ̈aisches Bildungszentrum (ebz) Otzenhausen, Germany, October 8{ 10, 1998. The Conference was sponsored by the Japanese Society for Arti cial Intelligence (JSAI) and the University of Kaiserslautern. Thirty-four papers on all aspects of algorithmic learning theory and related areas were submitted, all electronically. Twenty-six papers were accepted by the program committee based on originality, quality, and relevance to the theory of machine learning. Additionally, three invited talks presented by Akira Maruoka of Tohoku University, Arun Sharma of the University of New South Wales, and Stefan Wrobel from GMD, respectively, were featured at the conference. We would like to express our sincere gratitude to our invited speakers for sharing with us their insights on new and exciting developments in their areas of research. This conference is the ninth in a series of annual meetings established in 1990. The ALT series focuses on all areas related to algorithmic learning theory including (but not limited to): the theory of machine learning, the design and analysis of learning algorithms, computational logic of/for machine discovery, inductive inference of recursive functions and recursively enumerable languages, learning via queries, learning by arti cial and biological neural networks, pattern recognition, learning by analogy, statistical learning, Bayesian/MDL estimation, inductive logic programming, robotics, application of learning to databases, and gene analyses.

Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Ming Li
Publisher:
ISBN: 9783662203767
Category :
Languages : en
Pages : 484

Get Book Here

Book Description


Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Nader H. Bshouty
Publisher: Springer
ISBN: 3642341063
Category : Computers
Languages : en
Pages : 391

Get Book Here

Book Description
This book constitutes the refereed proceedings of the 23rd International Conference on Algorithmic Learning Theory, ALT 2012, held in Lyon, France, in October 2012. The conference was co-located and held in parallel with the 15th International Conference on Discovery Science, DS 2012. The 23 full papers and 5 invited talks presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on inductive inference, teaching and PAC learning, statistical learning theory and classification, relations between models and data, bandit problems, online prediction of individual sequences, and other models of online learning.

Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: José L. Balcázar
Publisher: Springer Science & Business Media
ISBN: 3540466495
Category : Computers
Languages : en
Pages : 405

Get Book Here

Book Description
This book constitutes the refereed proceedings of the 17th International Conference on Algorithmic Learning Theory, ALT 2006, held in Barcelona, Spain in October 2006, colocated with the 9th International Conference on Discovery Science, DS 2006. The 24 revised full papers presented together with the abstracts of five invited papers were carefully reviewed and selected from 53 submissions. The papers are dedicated to the theoretical foundations of machine learning.