Multistrategy Learning

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Multistrategy Learning
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Author : Ryszard S. Michalski
language : en
Publisher: Springer Science & Business Media
Release Date : 1993-06-30
Multistrategy Learning written by Ryszard S. Michalski 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 1993-06-30 with Computers categories.
Most machine learning research has been concerned with the development of systems that implememnt one type of inference within a single representational paradigm. Such systems, which can be called monostrategy learning systems, include those for empirical induction of decision trees or rules, explanation-based generalization, neural net learning from examples, genetic algorithm-based learning, and others. Monostrategy learning systems can be very effective and useful if learning problems to which they are applied are sufficiently narrowly defined. Many real-world applications, however, pose learning problems that go beyond the capability of monostrategy learning methods. In view of this, recent years have witnessed a growing interest in developing multistrategy systems, which integrate two or more inference types and/or paradigms within one learning system. Such multistrategy systems take advantage of the complementarity of different inference types or representational mechanisms. Therefore, they have a potential to be more versatile and more powerful than monostrategy systems. On the other hand, due to their greater complexity, their development is significantly more difficult and represents a new great challenge to the machine learning community. Multistrategy Learning contains contributions characteristic of the current research in this area.
Goal Driven Learning
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Author : Ashwin Ram
language : en
Publisher: MIT Press
Release Date : 1995
Goal Driven Learning written by Ashwin Ram and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.
Brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. It collects and solidifies existing results on this important issue in machine and human learning and presents a theoretical framework for future investigations. The book opens with an an overview of goal-driven learning research and computational and cognitive models of the goal-driven learning process. This introduction is followed by a collection of fourteen recent research articles addressing fundamental issues of the field, including psychological and functional arguments for modeling learning as a deliberative, planful process; experimental evaluation of the benefits of utility-based analysis to guide decisions about what to learn; case studies of computational models in which learning is driven by reasoning about learning goals; psychological evidence for human goal-driven learning; and the ramifications of goal-driven learning in educational contexts. The second part of the book presents six position papers reflecting ongoing research and current issues in goal-driven learning. Issues discussed include methods for pursuing psychological studies of goal-driven learning, frameworks for the design of active and multistrategy learning systems, and methods for selecting and balancing the goals that drive learning. A Bradford Book
Machine Learning
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Author : Ryszard S. Michalski
language : en
Publisher: Morgan Kaufmann
Release Date : 1994-02-09
Machine Learning written by Ryszard S. Michalski and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-02-09 with Computers categories.
Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area. See below for earlier volumes in the series.
Machine Learning
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Author : Yves Kodratoff
language : en
Publisher: Elsevier
Release Date : 2014-06-28
Machine Learning written by Yves Kodratoff and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.
Machine Learning Ecml 94
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Author : Francesco Bergadano
language : en
Publisher: Springer Science & Business Media
Release Date : 1994-03-22
Machine Learning Ecml 94 written by Francesco Bergadano 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 1994-03-22 with Computers categories.
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.
Understanding Language Understanding
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Author : Ashwin Ram
language : en
Publisher: MIT Press
Release Date : 1999
Understanding Language Understanding written by Ashwin Ram and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Language Arts & Disciplines categories.
This book highlights cutting-edge research relevant to the building of a computational model of reading comprehension, as in the processing and understanding of a natural language text or story. The book takes an interdisciplinary approach to the study of reading, with contributions from computer science, psychology, and philosophy. Contributors cover the theoretical and psychological foundations of the research in discussions of what it means to understand a text, how one builds a computational model, and related issues in knowledge representation and reasoning. The book also addresses some of the broader issues that a natural language system must deal with, such as reading in context, linguistic novelty, and information extraction.
Machine Learning And Its Applications
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Author : Georgios Paliouras
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-08-01
Machine Learning And Its Applications written by Georgios Paliouras 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 2001-08-01 with Computers categories.
In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.
Advances In Case Based Reasoning
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Author : Jean-Paul Haton
language : en
Publisher: Springer Science & Business Media
Release Date : 1995-10-11
Advances In Case Based Reasoning written by Jean-Paul Haton 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 1995-10-11 with Computers categories.
The type of material considered for publication includes drafts of original papers or monographs, technical reports of high quality and broad interest, advanced-level lectures, reports of meetings, provided they are of exceptional interest and focused on a single topic.
The 1995 Goddard Conference On Space Applications Of Artificial Intelligence And Emerging Information Technologies
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Author : Carl F. Hostetter
language : en
Publisher:
Release Date : 1995
The 1995 Goddard Conference On Space Applications Of Artificial Intelligence And Emerging Information Technologies written by Carl F. Hostetter and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with categories.
Information Processing For Remote Sensing
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Author : Chi Hau Chen
language : en
Publisher: World Scientific
Release Date : 1999-12-28
Information Processing For Remote Sensing written by Chi Hau Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-12-28 with Technology & Engineering categories.
This book provides the most comprehensive study of information processing techniques and issues in remote sensing. Topics covered include image and signal processing, pattern recognition and feature extraction for remote sensing, neural networks and wavelet transforms in remote sensing, remote sensing of ocean and coastal environment, SAR image filtering and segmentation, knowledge-based systems, software and hardware issues, data compression, change detection, etc. Emphasis is placed on environmental issues of remote sensing.With 58 color illustrations.