Book Description
Machine Learning Proceedings 1989
Author : Alberto Maria Segre
Publisher : Morgan Kaufmann
Page : 521 pages
File Size : 28,23 MB
Release : 2014-06-28
Category : Computers
ISBN : 1483297403
Machine Learning Proceedings 1989
Author : Douglas Medin
Publisher : John Wiley & Sons
Page : 658 pages
File Size : 29,27 MB
Release : 2004-02-05
Category : Psychology
ISBN : 9780471650157
Now available in paperback. This revised and updated edition of the definitive resource for experimental psychology offers comprehensive coverage of the latest findings in the field, as well as the most recent contributions in methodology and the explosion of research in neuroscience. Volume Two: Memory and Cognitive Processes, focuses on the neurological and cognitive processes on topics such as memory, decision-making, spatial cognition, linguistics, reasoning, and concepts.
Author : Allen Kent
Publisher : CRC Press
Page : 404 pages
File Size : 35,41 MB
Release : 1998-10-30
Category : Computers
ISBN : 9780824727192
Applications of Negotiating and Learning Agents to User Query Performance with Database Feedback
Author : Sebastian Thrun
Publisher : Springer Science & Business Media
Page : 274 pages
File Size : 19,17 MB
Release : 2012-12-06
Category : Computers
ISBN : 1461313813
Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. As a result, EBNN generalizes more accurately from less data than comparable methods. Explanation-Based Neural Network Learning: A Lifelong Learning Approach describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess. `The paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. Given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm.' From the Foreword by Tom M. Mitchell.
Author :
Publisher :
Page : 552 pages
File Size : 36,16 MB
Release : 1991
Category : Cognitive science
ISBN :
Author : Stephen Muggleton
Publisher : Morgan Kaufmann
Page : 602 pages
File Size : 38,53 MB
Release : 1992
Category : Computers
ISBN : 9780125097154
Inductive logic programming is a new research area emerging at present. Whilst inheriting various positive characteristics of the parent subjects of logic programming an machine learning, it is hoped that the new area will overcome many of the limitations of its forbears. This book describes the theory, implementations and applications of Inductive Logic Programming.
Author : William W. Cohen
Publisher : Morgan Kaufmann
Page : 398 pages
File Size : 13,88 MB
Release : 2014-06-28
Category : Computers
ISBN : 1483298183
Machine Learning Proceedings 1994
Author : Lawrence A. Birnbaum
Publisher : Morgan Kaufmann
Page : 361 pages
File Size : 32,64 MB
Release : 2014-05-23
Category : Computers
ISBN : 1483298620
Machine Learning Proceedings 1993
Author : Ashwin Ram
Publisher : MIT Press
Page : 548 pages
File Size : 37,90 MB
Release : 1995
Category : Computers
ISBN : 9780262181655
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
Author : Allen Kent
Publisher : CRC Press
Page : 396 pages
File Size : 25,98 MB
Release : 2000-09-21
Category : Language Arts & Disciplines
ISBN : 9780824720681
This is the 68th volume (supplement 31) in a series which examines library and information science.