Algorithmic Learning
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Algorithmic Learning In A Random World
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Author : Vladimir Vovk
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-03-22
Algorithmic Learning In A Random World written by Vladimir Vovk and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-03-22 with Computers categories.
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.
Algorithmic Learning Theory
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Author : Michael M. Richter
language : en
Publisher: Springer Science & Business Media
Release Date : 1998
Algorithmic Learning Theory written by Michael M. Richter and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Artificial intelligence categories.
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
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Author : Marcus Hutter
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-09-27
Algorithmic Learning Theory written by Marcus Hutter and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-27 with Computers categories.
This volume contains the papers presented at the 21st International Conf- ence on Algorithmic Learning Theory (ALT 2010), which was held in Canberra, Australia, October 6–8, 2010. The conference was co-located with the 13th - ternational Conference on Discovery Science (DS 2010) and with the Machine Learning Summer School, which was held just before ALT 2010. The tech- cal program of ALT 2010, contained 26 papers selected from 44 submissions and ?ve invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2010 was dedicated to the theoretical foundations of machine learning and took place on the campus of the Australian National University, Canberra, Australia. ALT provides a forum for high-quality talks with a strong theore- cal background and scienti?c interchange in areas such as inductive inference, universal prediction, teaching models, grammatical inference, formal languages, inductive logic programming, query learning, complexity of learning, on-line learning and relative loss bounds, semi-supervised and unsupervised learning, clustering,activelearning,statisticallearning,supportvectormachines,Vapnik- Chervonenkisdimension,probablyapproximatelycorrectlearning,Bayesianand causal networks, boosting and bagging, information-based methods, minimum descriptionlength,Kolmogorovcomplexity,kernels,graphlearning,decisiontree methods, Markov decision processes, reinforcement learning, and real-world - plications of algorithmic learning theory. DS 2010 was the 13th International Conference on Discovery Science and focused on the development and analysis of methods for intelligent data an- ysis, knowledge discovery and machine learning, as well as their application to scienti?c knowledge discovery. As is the tradition, it was co-located and held in parallel with Algorithmic Learning Theory.
Algorithmic Learning Theory
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Author : Sanjay Jain
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-09-26
Algorithmic Learning Theory written by Sanjay Jain and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-09-26 with Computers categories.
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
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Author : Hiroki Arimura
language : en
Publisher: Springer
Release Date : 2003-06-29
Algorithmic Learning Theory written by Hiroki Arimura and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-29 with Computers categories.
This book constitutes the refereed proceedings of the 11th International Conference on Algorithmic Learning Theory, ALT 2000, held in Sydney, Australia in December 2000. The 22 revised full papers presented together with three invited papers were carefully reviewed and selected from 39 submissions. The papers are organized in topical sections on statistical learning, inductive logic programming, inductive inference, complexity, neural networks and other paradigms, support vector machines.
Algorithmic Learning Theory
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Author : Yoav Freund
language : en
Publisher: Springer
Release Date : 2008-10-02
Algorithmic Learning Theory written by Yoav Freund and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-02 with Computers categories.
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
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Author : Ricard Gavaldà
language : en
Publisher: Springer
Release Date : 2003-09-25
Algorithmic Learning Theory written by Ricard Gavaldà and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-09-25 with Computers categories.
This book constitutes the refereed proceedings of the 14th International Conference on Algorithmic Learning Theory, ALT 2003, held in Sapporo, Japan in October 2003. The 19 revised full papers presented together with 2 invited papers and abstracts of 3 invited talks were carefully reviewed and selected from 37 submissions. The papers are organized in topical sections on inductive inference, learning and information extraction, learning with queries, learning with non-linear optimization, learning from random examples, and online prediction.
Algorithmic Learning Theory
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Author : Kamalika Chaudhuri
language : en
Publisher: Springer
Release Date : 2015-10-04
Algorithmic Learning Theory written by Kamalika Chaudhuri and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-04 with Computers categories.
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
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Author : Setsuo Arikawa
language : en
Publisher: Springer Science & Business Media
Release Date : 1994-09-28
Algorithmic Learning Theory written by Setsuo Arikawa and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-09-28 with Computers categories.
This volume presents the proceedings of the Fourth International Workshop on Analogical and Inductive Inference (AII '94) and the Fifth International Workshop on Algorithmic Learning Theory (ALT '94), held jointly at Reinhardsbrunn Castle, Germany in October 1994. (In future the AII and ALT workshops will be amalgamated and held under the single title of Algorithmic Learning Theory.) The book contains revised versions of 45 papers on all current aspects of computational learning theory; in particular, algorithmic learning, machine learning, analogical inference, inductive logic, case-based reasoning, and formal language learning are addressed.
Algorithmic Learning Theory
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Author : Arun K. Sharma
language : en
Publisher: Springer Science & Business Media
Release Date : 1996-10-09
Algorithmic Learning Theory written by Arun K. Sharma and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-10-09 with Computers categories.
This book constitutes the refereed proceedings of the 7th International Workshop on Algorithmic Learning Theory, ALT '96, held in Sydney, Australia, in October 1996. The 16 revised full papers presented were selected from 41 submissions; also included are eight short papers as well as four full length invited contributions by Ross Quinlan, Takeshi Shinohara, Leslie Valiant, and Paul Vitanyi, and an introduction by the volume editors. The book covers all areas related to algorithmic learning theory, ranging from theoretical foundations of machine learning to applications in several areas.