Machine Learning: ECML-93
Machine Learning: ECML-93

Author: Pavel B. Brazdil

Publisher: Springer Science & Business Media

Published: 1993-03-23

Total Pages: 492

ISBN-13: 9783540566021

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This volume contains the proceedings of the Eurpoean Conference on Machine Learning (ECML-93), continuing the tradition of the five earlier EWSLs (European Working Sessions on Learning). The aim of these conferences is to provide a platform for presenting the latest results in the area of machine learning. The ECML-93 programme included invited talks, selected papers, and the presentation of ongoing work in poster sessions. The programme was completed by several workshops on specific topics. The volume contains papers related to all these activities. The first chapter of the proceedings contains two invited papers, one by Ross Quinlan and one by Stephen Muggleton on inductive logic programming. The second chapter contains 18 scientific papers accepted for the main sessions of the conference. The third chapter contains 18 shorter position papers. The final chapter includes three overview papers related to the ECML-93 workshops.

Machine Learning: ECML-94
Machine Learning: ECML-94

Author: Francesco Bergadano

Publisher: Springer Science & Business Media

Published: 1994-03-22

Total Pages: 460

ISBN-13: 9783540578680

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This volume contains the proceedings of the European Conference on Machine Learning 1994, which continues the tradition of earlier meetings and which is a major forum for the presentation of the latest and most significant results in machine learning. Machine learning is one of the most important subfields of artificial intelligence and computer science, as it is concerned with the automation of learning processes. This volume contains two invited papers, 19 regular papers, and 25 short papers carefully reviewed and selected from in total 88 submissions. The papers describe techniques, algorithms, implementations, and experiments in the area of machine learning.

Machine Learning: ECML-95
Machine Learning: ECML-95

Author: Nada Lavrač

Publisher: Springer Science & Business Media

Published: 1995-04-05

Total Pages: 388

ISBN-13: 9783540592860

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This volume constitutes the proceedings of the Eighth European Conference on Machine Learning ECML-95, held in Heraclion, Crete in April 1995. Besides four invited papers the volume presents revised versions of 14 long papers and 26 short papers selected from a total of 104 submissions. The papers address all current aspects in the area of machine learning; also logic programming, planning, reasoning, and algorithmic issues are touched upon.

Algorithmic Learning Theory
Algorithmic Learning Theory

Author: Klaus P. Jantke

Publisher: Springer Science & Business Media

Published: 1993-10-20

Total Pages: 444

ISBN-13: 9783540573708

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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.

Extensions of Logic Programming
Extensions of Logic Programming

Author: Roy Dyckhoff

Publisher: Springer Science & Business Media

Published: 1994-05-20

Total Pages: 376

ISBN-13: 9783540580256

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The papers in this volume are extended versions of presentations at the fourth International Workshop on Extensions of Logic Programming, held at the University of St Andrews, March/April 1993. Among the topics covered in the volume are: defintional reflection and completion, modules in lambda-Prolog, representation of logics as partial inductive definitions, non-procedural logic programming, knowledge representation, contradiction avoidance, disjunctive databases, strong negation, linear logic programming, proof theory and regular search spaces, finite sets and constraint logic programming, search-space pruning and universal algebra, and implementation on transputer networks.

Algorithms - ESA '93
Algorithms - ESA '93

Author: Thomas Lengauer

Publisher: Springer Science & Business Media

Published: 1993-09-21

Total Pages: 434

ISBN-13: 9783540572732

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Symposium on Algorithms (ESA '93), held in Bad Honnef, near Boon, in Germany, September 30 - October 2, 1993. The symposium is intended to launchan annual series of international conferences, held in early fall, covering the field of algorithms. Within the scope of the symposium lies all research on algorithms, theoretical as well as applied, that is carried out in the fields of computer science and discrete applied mathematics. The symposium aims to cater to both of these research communities and to intensify the exchange between them. The volume contains 35 contributed papers selected from 101 proposals submitted in response to the call for papers, as well as three invited lectures: "Evolution of an algorithm" by Michael Paterson, "Complexity of disjoint paths problems in planar graphs" by Alexander Schrijver, and "Sequence comparison and statistical significance in molecular biology" by Michael S. Waterman.

Foundations of Rule Learning
Foundations of Rule Learning

Author: Johannes Fürnkranz

Publisher: Springer Science & Business Media

Published: 2012-11-06

Total Pages: 345

ISBN-13: 3540751971

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Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.

Conceptual Graphs for Knowledge Representation
Conceptual Graphs for Knowledge Representation

Author: Guy W. Mineau

Publisher: Springer Science & Business Media

Published: 1993-07-14

Total Pages: 470

ISBN-13: 9783540569794

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Artificial Intelligence and cognitive science are the two fields devoted to the study and development of knowledge-based systems (KBS). Over the past 25years, researchers have proposed several approaches for modeling knowledge in KBS, including several kinds of formalism such as semantic networks, frames, and logics. In the early 1980s, J.F. Sowa introduced the conceptual graph (CG) theory which provides a knowledge representation framework consisting of a form of logic with a graph notationand integrating several features from semantic net and frame representations. Since that time, several research teams over the world have been working on the application and extension of CG theory in various domains ranging from natural language processing to database modeling and machine learning. This volume contains selected papers fromthe international conference on Conceptual Structures held in the city of Quebec, Canada, August 4-7, 1993. The volume opens with invited papers by J.F. Sowa, B.R. Gaines, and J. Barwise.