Algorithmic Learning Theory

The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory.

Algorithmic Learning Theory

Author: Shai Ben David

Publisher: Springer Science & Business Media

ISBN: 3540233563

Page: 514

View: 402

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.

Algorithmic Learning Theory

This book constitutes the refereed proceedings of the 11th International Conference on Algorithmic Learning Theory, ALT 2000, held in Sydney, Australia in December 2000.

Algorithmic Learning Theory

Author: Hiroki Arimura

Publisher: Springer Science & Business Media

ISBN: 9783540412373

Page: 348

View: 800

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 II

In recent years it has been recognized that learning ability would be indispensable to further new development of intelligent computer software and information systems.

Algorithmic Learning Theory II

Author: Setsuo Arikawa

Publisher: IOS Press

ISBN: 9789051990881

Page: 307

View: 900

In recent years it has been recognized that learning ability would be indispensable to further new development of intelligent computer software and information systems. The need for new logical tools in learning theory and machine learning is also broadly acknowledged.

Algorithmic Learning Theory

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 ...

Algorithmic Learning Theory

Author: Yoav Freund

Publisher: Springer Science & Business Media

ISBN: 3540879862

Page: 467

View: 893

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.

Algorithmic Learning Theory

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, ...

Algorithmic Learning Theory

Author: Michael M. Richter

Publisher: Springer

ISBN: 3540497307

Page: 444

View: 940

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

This book constitutes the refereed proceedings of the 20th International Conference on Algorithmic Learning Theory, ALT 2009, held in Porto, Portugal, in October 2009, co-located with the 12th International Conference on Discovery Science, ...

Algorithmic Learning Theory

Author: Ricard Gavaldà

Publisher: Springer Science & Business Media

ISBN: 3642044131

Page: 399

View: 514

As intelligent autonomous agents and multiagent system applications become more pervasive, it becomes increasingly important to understand the risks associated with using these systems. Incorrect or inappropriate agent behavior can have harmful - fects, including financial cost, loss of data, and injury to humans or systems. For - ample, NASA has proposed missions where multiagent systems, working in space or on other planets, will need to do their own reasoning about safety issues that concern not only themselves but also that of their mission. Likewise, industry is interested in agent systems that can search for new supply opportunities and engage in (semi-) automated negotiations over new supply contracts. These systems should be able to securely negotiate such arrangements and decide which credentials can be requested and which credentials may be disclosed. Such systems may encounter environments that are only partially understood and where they must learn for themselves which aspects of their environment are safe and which are dangerous. Thus, security and safety are two central issues when developing and deploying such systems. We refer to a multiagent system s security as the ability of the system to deal with threats that are intentionally caused by other intelligent agents and/or s- tems, and the system s safety as its ability to deal with any other threats to its goals."

Algorithmic Learning Theory

Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger, and
Thomas Zeugmann. Learning one-variable pattern languages very efficiently on
average, in parallel, and by asking queries. In Algorithmic Learning Theory: ALT '
97, ...

Algorithmic Learning Theory

Author: Osamu Watanabe

Publisher: Springer

ISBN: 3540467696

Page: 372

View: 377

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.

Induction Algorithmic Learning Theory and Philosophy

This is the first book to collect essays from philosophers, mathematicians and computer scientists working at the exciting interface of algorithmic learning theory and the epistemology of science and inductive inference.

Induction  Algorithmic Learning Theory  and Philosophy

Author: Michèle Friend

Publisher: Springer Science & Business Media

ISBN: 1402061277

Page: 290

View: 926

This is the first book to collect essays from philosophers, mathematicians and computer scientists working at the exciting interface of algorithmic learning theory and the epistemology of science and inductive inference. Readable, introductory essays provide engaging surveys of different, complementary, and mutually inspiring approaches to the topic, both from a philosophical and a mathematical viewpoint.

Algorithmic Learning Theory

Algorithmic Learning Theory

Author: Ming Li

Publisher: Springer

ISBN: 9783540635772

Page: 470

View: 342

This book constitutes the refereed proceedings of the 8th International Workshop on Algorithmic Learning Theory, ALT'97, held in Sendai, Japan, in October 1997. The volume presents 26 revised full papers selected from 42 submissions. Also included are three invited papers by leading researchers. Among the topics addressed are PAC learning, learning algorithms, inductive learning, inductive inference, learning from examples, game-theoretical aspects, decision procedures, language learning, neural algorithms, and various other aspects of computational learning theory.

Algorithmic Learning Theory

This book constitutes the refereed proceedings of the 7th International Workshop on Algorithmic Learning Theory, ALT '96, held in Sydney, Australia, in October 1996.

Algorithmic Learning Theory

Author: Australia) Alt 9 (1996 Sydney

Publisher: Springer Science & Business Media

ISBN: 9783540618638

Page: 336

View: 711

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.

Algorithmic Learning Theory

The algorithm could find an estimate of D ( f ) by successive approximations
obtained by iterations of the POSQ ... In ALT 99 , 10th International Conference
on Algorithmic Learning Theory , volume 1720 of LNAI , pages 219 - 230 , 1999 .

Algorithmic Learning Theory

Author:

Publisher:

ISBN:

Page:

View: 440

Algorithmic Learning Theory

This volume contains the papers presented at the 13th Annual Conference on Algorithmic Learning Theory (ALT 2002), which was held in Lub ̈ eck (Germany) during November 24–26, 2002.

Algorithmic Learning Theory

Author: Nicolò Cesa-Bianchi

Publisher: Springer Science & Business Media

ISBN: 9783540001706

Page: 413

View: 986

This book constitutes the refereed proceedings of the 13th International Conference on Algorithmic Learning Theory, ALT 2002, held in Lübeck, Germany in November 2002. The 26 revised full papers presented together with 5 invited contributions and an introduction were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on learning Boolean functions, boosting and margin-based learning, learning with queries, learning and information extraction, inductive inference, inductive logic programming, language learning, statistical learning, and applications and heuristics.

Algorithmic Learning Theory

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.

Algorithmic Learning Theory

Author: Naoki Abe

Publisher: Elsevier

ISBN: 9783540428756

Page: 377

View: 714

This book constitutes the refereed proceedings of the 12th International Conference on Algorithmic Learning Theory, ALT 2001, held in Washington, DC, USA in November 2001. The 21 revised full papers presented together with two invited papers and an introduction by the volume editors were carefully reviewed and selected from 42 submissions. The papers are organized in topical sections on complexity of learning, support vector machines, new learning models, online learning, inductive inference, refutable inductive inference, learning structures and languages.

Algorithmic Learning Theory

This book constitutes the refereed proceedings of the 6th International Workshop on Algorithmic Learning Theory, ALT '95, held in Fukuoka, Japan, in October 1995.

Algorithmic Learning Theory

Author: Klaus P. Jantke

Publisher: Boom Koninklijke Uitgevers

ISBN: 9783540604549

Page: 318

View: 607

This book constitutes the refereed proceedings of the 6th International Workshop on Algorithmic Learning Theory, ALT '95, held in Fukuoka, Japan, in October 1995. The book contains 21 revised full papers selected from 46 submissions together with three invited contributions. It covers all 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, artificial and biologicial neural network learning, pattern recognition, learning by analogy, statistical learning, inductive logic programming, robot learning, and gene analysis.

Algorithmic Learning Theory

The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks.

Algorithmic Learning Theory

Author: Ronald Ortner

Publisher: Springer

ISBN: 9783319463780

Page: 371

View: 360

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. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.

Algorithmic Learning Theory

This volume contains the 31 papers presented at the first international workshop on Algorithmic Learning Theory (ALT '90) which was held in Tokyo, 8-10 October 1990.

Algorithmic Learning Theory

Author: Setsuo Arikawa

Publisher: Springer

ISBN: 9783540196617

Page: 441

View: 862

This volume contains the 31 papers presented at the first international workshop on Algorithmic Learning Theory (ALT '90) which was held in Tokyo, 8-10 October 1990. This workshop was the first meeting on this subject sponsored by the Japanese Society for Artificial Intelligence, and it is expected that future ALT workshops will be held every two years. Recent research on AI systems has indicated that 'learning ability' is fundamental to the development of intelligent computer software and of information systems in areas such as natural language understanding, pattern recognition, and robotics. The main aim of this workshop was to provide an open forum for intensive discussions and the exchange of academic information among researchers in the area of algorithmic learning theory. From the 46 extended abstracts submitted, 28 papers were selected for inclusion in this volume, with authors from the USA, the UK, Japan, the USSR, India, and continental Europe. Besides the 28 selected papers, the program committee invited 3 lectures by distinguished researchers: "Mathematical Theory of Neural Learning" (by S. Amari, University of Tokyo), "Decision Theoretic Generalizations of the PAC Learning Model" (by D. Haussler, University of California), and "Inductive Logic Programming" (by S. Muggleton, The Turing Institute, Glasgow).