| 书目名称 | Soft Computing for Knowledge Discovery | | 副标题 | Introducing Cartesia | | 编辑 | James G. Shanahan | | 视频video | | | 丛书名称 | The Springer International Series in Engineering and Computer Science | | 图书封面 |  | | 描述 | Knowledge discovery is an area of computer science thatattempts to uncover interesting and useful patterns in data thatpermit a computer to perform a task autonomously or assist a human inperforming a task more efficiently.. .Soft Computing for Knowledge Discovery. provides a self-containedand systematic exposition of the key theory and algorithms that formthe core of knowledge discovery from a soft computing perspective. Itfocuses on knowledge representation, machine learning, and the keymethodologies that make up the fabric of soft computing - fuzzy settheory, fuzzy logic, evolutionary computing, and various theories ofprobability (e.g. naïve Bayes and Bayesian networks,Dempster-Shafer theory, mass assignment theory, and others). Inaddition to describing many state-of-the-art soft computing approachesto knowledge discovery, the author introduces .Cartesian granule..features. and their corresponding learning algorithms as anintuitive approach to knowledge discovery. This new approach embracesthe synergistic spirit of soft computing and exploits uncertainty inorder to achieve tractability, transparency and generalization.Parallels are drawn between this approach and other well know | | 出版日期 | Book 2000 | | 关键词 | Bayesian network; algorithms; cognition; computer science; evolution; fuzzy; fuzzy logic; information syste | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4615-4335-0 | | isbn_softcover | 978-1-4613-6947-9 | | isbn_ebook | 978-1-4615-4335-0Series ISSN 0893-3405 | | issn_series | 0893-3405 | | copyright | Springer Science+Business Media New York 2000 |
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发表于 2025-3-21 18:12:49
