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【分享】Foundations of Knowledge Acquisition_Machine Learning.1993
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Foundations of Knowledge Acquisition_Machine Learning 免责声明 本资源来自于互联网，仅供网络测试之用，请务必在下载后24小时内删除！所有资源不涉及任何商业用途。发帖人不承担由下载使用者引发的一切法律责任及连带责任！ 著作权归原作者或出版社所有。未经发贴人conanwj许可，严禁任何人以任何形式转贴本文，违者必究！ 如果本帖侵犯您的著作权，请与conanwj联系，收到通知后我们将立即删除此帖！ Authors(Editors): Alan L. Meyrowitz Naval Research Laboratory Susan Chipman Office of Naval Research eds Publisher: Kluwer Academic Pub Date: 1993 Pages: 341 ISBN: ISBN 0-7923-9277-9 (v. 1) ISBN 0-7923-9278-7 (v. 2) Foreword One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e.g., personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e.g., imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact of successful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain about the methods by which machines and humans might learn, significant progress has been made. For example, learning systems have been demonstrated for tasks such as learning how to drive a vehicle along a roadway (one has successfully driven at 55 mph for 20 miles on a public highway), for learning to evaluate financial loan applications (such systems are now in commercial use), and for learning to recognize human speech (today's top speech recognition systems all employ learning methods). At the same time, a theoretical understanding of learning has begun to appear. For example, we now can place theoretical bounds on the amount of training data a learner must observe in order to reduce its risk of choosing an incorrect hypothesis below some desired threshold. And an improved understanding of human learning is beginning to emerge alongside our improved understanding of machine learning. For example, we now have models of how human novices learn to become experts at various tasks ~ models that have been implemented as precise computer programs, and that generate traces very much like those observed in human protocols. The book you are holding describes a variety of these new results. This work has been pursued under research funding from the Office of Naval Research (ONR) during the time that the editors of this book managed an Accelerated Research Initiative in this area. While several government and private organizations have been important in supporting machine learning research, this ONR effort stands out in particular for its farsighted vision in selecting research topics. During a period when much funding for basic research was being rechanneled to shorter-term development and demonstration projects, ONR had the vision to continue its tradition of supporting research of fundamental long-range significance. The results represent real progress on central problems of machine learning. I encourage you to explore them for yourself in the following chapters. Tom Mitchell Carnegie Mellon University 本资源链接共6个可选网络硬盘链接，16.02 MB。 -------------------------------------------------------------------------------------------------------- 16.02 Foundations of Knowledge Acquisition_Machine Learning.9780792392781.p352.Springer.1993.rar https://rapidshare.com/files/389 ... 2.Springer.1993.rar https://uploading.com/files/d9cb ... .Springer.1993.rar/ https://www.easy-share.com/1910299666/Foundations of Knowledge Acquisition_Machine Learning.9780792392781.p352.Springer.1993.rar https://depositfiles.com/files/829h4a0vg https://www.divshare.com/download/11428327-c4a https://www.sendspace.com/file/kvt015 -------------------------------------------------------------------------------------------------------- [ Last edited by conanwj on 2010-9-15 at 23:07 ] |
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