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Author : Setsuo Arikawa
Publisher : IOS Press
ISBN 13 : 9784274076992
Total Pages : 324 pages
Book Rating : 4.0/5 (769 download)
Download or read book Algorithmic Learning Theory II written by Setsuo Arikawa and published by IOS Press. This book was released on 1992 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Setsuo Arikawa
Publisher :
ISBN 13 : 9783662188941
Total Pages : 364 pages
Book Rating : 4.1/5 (889 download)
Download or read book Algorithmic Learning Theory written by Setsuo Arikawa and published by . This book was released on 2014-01-15 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Setsuo Arikawa
Publisher : Springer
ISBN 13 :
Total Pages : 464 pages
Book Rating : 4.E/5 ( download)
Download or read book Algorithmic Learning Theory written by Setsuo Arikawa and published by Springer. This book was released on 1990 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Setsuo Arikawa
Publisher : Springer Science & Business Media
ISBN 13 : 9783540585206
Total Pages : 600 pages
Book Rating : 4.5/5 (852 download)
Download or read book Algorithmic Learning Theory written by Setsuo Arikawa and published by Springer Science & Business Media. This book was released on 1994-09-28 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Shai Ben David
Publisher : Springer Science & Business Media
ISBN 13 : 3540233563
Total Pages : 519 pages
Book Rating : 4.5/5 (42 download)
Download or read book Algorithmic Learning Theory written by Shai Ben David and published by Springer Science & Business Media. This book was released on 2004-09-23 with total page 519 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.
Author : Robert E. Schapire
Publisher : MIT Press
ISBN 13 : 0262526034
Total Pages : 544 pages
Book Rating : 4.2/5 (625 download)
Download or read book Boosting written by Robert E. Schapire and published by MIT Press. This book was released on 2014-01-10 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate “rules of thumb.” A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.
Author : Sanjay Jain
Publisher : Springer Science & Business Media
ISBN 13 : 354029242X
Total Pages : 502 pages
Book Rating : 4.5/5 (42 download)
Download or read book Algorithmic Learning Theory written by Sanjay Jain and published by Springer Science & Business Media. This book was released on 2005-09-26 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Naoki Abe
Publisher : Springer
ISBN 13 : 3540455833
Total Pages : 388 pages
Book Rating : 4.5/5 (44 download)
Download or read book Algorithmic Learning Theory written by Naoki Abe and published by Springer. This book was released on 2003-06-30 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. The main objective of the conference is to provide an inter-disciplinary forum for the discussion of theoretical foundations of machine learning, as well as their relevance to practical applications. The conference was co-located with the Fourth International Conference on Discovery Science (DS 2001). The volume includes 21 contributed papers. These papers were selected by the program committee from 42 submissions based on clarity, signi?cance, o- ginality, and relevance to theory and practice of machine learning. Additionally, the volume contains the invited talks of ALT 2001 presented by Dana Angluin of Yale University, USA, Paul R. 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. Furthermore, this volume includes abstracts of the invited talks for DS 2001 presented by Lindley Darden and Ben Shneiderman both of the University of Maryland at College Park, USA. The complete versions of these papers are published in the DS 2001 proceedings (Lecture Notes in Arti?cial Intelligence Vol. 2226).
Author : Yoav Freund
Publisher : Springer Science & Business Media
ISBN 13 : 3540879862
Total Pages : 480 pages
Book Rating : 4.5/5 (48 download)
Download or read book Algorithmic Learning Theory written by Yoav Freund and published by Springer Science & Business Media. This book was released on 2008-09-29 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008. The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.
Author : José L. Balcázar
Publisher : Springer Science & Business Media
ISBN 13 : 3540466495
Total Pages : 405 pages
Book Rating : 4.5/5 (44 download)
Download or read book Algorithmic Learning Theory written by José L. Balcázar and published by Springer Science & Business Media. This book was released on 2006-09-27 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Michael M. Richter
Publisher : Springer Science & Business Media
ISBN 13 : 354065013X
Total Pages : 450 pages
Book Rating : 4.5/5 (46 download)
Download or read book Algorithmic Learning Theory written by Michael M. Richter and published by Springer Science & Business Media. This book was released on 1998 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Marcus Hutter
Publisher : Springer Science & Business Media
ISBN 13 : 3540752242
Total Pages : 415 pages
Book Rating : 4.5/5 (47 download)
Download or read book Algorithmic Learning Theory written by Marcus Hutter and published by Springer Science & Business Media. This book was released on 2007-09-17 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 18th International Conference on Algorithmic Learning Theory, ALT 2007, held in Sendai, Japan, October 1-4, 2007, co-located with the 10th International Conference on Discovery Science, DS 2007. The 25 revised full papers presented together with the abstracts of five invited papers were carefully reviewed and selected from 50 submissions. They are dedicated to the theoretical foundations of machine learning.
Author : Jyriki Kivinen
Publisher : Springer
ISBN 13 : 3642244122
Total Pages : 465 pages
Book Rating : 4.6/5 (422 download)
Download or read book Algorithmic Learning Theory written by Jyriki Kivinen and published by Springer. This book was released on 2011-10-07 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Arun K. Sharma
Publisher : Springer Science & Business Media
ISBN 13 : 9783540618638
Total Pages : 362 pages
Book Rating : 4.6/5 (186 download)
Download or read book Algorithmic Learning Theory written by Arun K. Sharma and published by Springer Science & Business Media. This book was released on 1996-10-09 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Osamu Watanabe
Publisher : Springer
ISBN 13 : 3540467696
Total Pages : 375 pages
Book Rating : 4.5/5 (44 download)
Download or read book Algorithmic Learning Theory written by Osamu Watanabe and published by Springer. This book was released on 2007-03-05 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Conference on Algorithmic Learning Theory, ALT'99, held in Tokyo, Japan, in December 1999. The 26 full papers presented were carefully reviewed and selected from a total of 51 submissions. Also included are three invited papers. The papers are organized in sections on Learning Dimension, Inductive Inference, Inductive Logic Programming, PAC Learning, Mathematical Tools for Learning, Learning Recursive Functions, Query Learning and On-Line Learning.
Author : Klaus P. Jantke
Publisher : Springer Science & Business Media
ISBN 13 : 9783540573708
Total Pages : 444 pages
Book Rating : 4.5/5 (737 download)
Download or read book Algorithmic Learning Theory written by Klaus P. Jantke and published by Springer Science & Business Media. This book was released on 1993-10-20 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation This volume contains the papers that were presented at theThird Workshop onAlgorithmic Learning Theory, held in Tokyoin October 1992. In addition to 3invited papers, the volumecontains 19 papers accepted for presentation, selected from29 submitted extended abstracts. The ALT workshops have beenheld annually since 1990 and are organized and sponsored bythe Japanese Society for Artificial Intelligence. The mainobjective of these workshops is to provide an open forum fordiscussions and exchanges of ideasbetween researchers fromvarious backgrounds in this emerging, interdisciplinaryfield of learning theory. The volume is organized into partson learning via query, neural networks, inductive inference, analogical reasoning, and approximate learning.
Author : Nader H. Bshouty
Publisher : Springer
ISBN 13 : 3642341063
Total Pages : 391 pages
Book Rating : 4.6/5 (423 download)
Download or read book Algorithmic Learning Theory written by Nader H. Bshouty and published by Springer. This book was released on 2012-10-01 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
