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Computational Learning Theory and Natural Learning Systems: Intersections between theory and experiment

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Release : 1994
Genre : Computational learning theory
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Computational Learning Theory and Natural Learning Systems: Intersections between theory and experiment by : George A. Drastal

Download or read book Computational Learning Theory and Natural Learning Systems: Intersections between theory and experiment written by George A. Drastal. This book was released on 1994. Available in PDF, EPUB and Kindle. Book excerpt:

Computational Learning Theory and Natural Learning Systems: Making learning systems practical

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Author :
Release : 1994
Genre : Computational learning theory
Kind : eBook
Book Rating : 180/5 ( reviews)

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Book Synopsis Computational Learning Theory and Natural Learning Systems: Making learning systems practical by : Russell Greiner

Download or read book Computational Learning Theory and Natural Learning Systems: Making learning systems practical written by Russell Greiner. This book was released on 1994. Available in PDF, EPUB and Kindle. Book excerpt: This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and Ǹatural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI). Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems. Contributors : Klaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E.M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S.V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador.

Computational Learning Theory and Natural Learning Systems

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Author :
Release : 1997
Genre : Machine learning
Kind : eBook
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Book Synopsis Computational Learning Theory and Natural Learning Systems by : Thomas Petsche

Download or read book Computational Learning Theory and Natural Learning Systems written by Thomas Petsche. This book was released on 1997. Available in PDF, EPUB and Kindle. Book excerpt:

Computational Learning Theory and Natural Learning Systems: Selecting good models

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Author :
Release : 1994
Genre : Computers
Kind : eBook
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Book Synopsis Computational Learning Theory and Natural Learning Systems: Selecting good models by : Stephen José Hanson

Download or read book Computational Learning Theory and Natural Learning Systems: Selecting good models written by Stephen José Hanson. This book was released on 1994. Available in PDF, EPUB and Kindle. Book excerpt: Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.

Goal-driven Learning

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Author :
Release : 1995
Genre : Computers
Kind : eBook
Book Rating : 655/5 ( reviews)

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Book Synopsis Goal-driven Learning by : Ashwin Ram

Download or read book Goal-driven Learning written by Ashwin Ram. This book was released on 1995. Available in PDF, EPUB and Kindle. Book excerpt: 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

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