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.
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:
Download or read book Algorithmic Learning Theory written by Ricard Gavaldà and published by Springer. This book was released on 2003-10-02 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
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.
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.
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:
Download or read book Algorithmic Learning Theory written by Jyriki Kivinen and published by Springer Science & Business Media. This book was released on 2011-09-23 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.
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.
Download or read book Algorithmic Learning Theory written by Michael M. Richter and published by Springer. This book was released on 2003-06-29 with total page 444 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.
Download or read book Algorithmic Learning Theory written by Hiroki Arimura and published by Springer. This book was released on 2003-06-29 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
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).
Download or read book Algorithmic Learning Theory written by Michael M. Richter and published by . This book was released on 2014-01-15 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 372 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.
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.
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.
