Advanced Data Mining and Applications: Third International Conference, ADMA 2007, Harbin, China, August 6-8, 2007 Proceedings (Lecture Notes in Computer Science)
暫譯: 進階資料探勘與應用:第三屆國際會議 ADMA 2007,中國哈爾濱,2007年8月6-8日 會議錄(計算機科學講義)

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  • 出版商: Springer
  • 出版日期: 2007-07-17
  • 售價: $6,020
  • 貴賓價: 9.5$5,719
  • 語言: 英文
  • 頁數: 408
  • 裝訂: Hardcover
  • ISBN: 3540738703
  • ISBN-13: 9783540738701
  • 相關分類: Computer-ScienceData-mining
  • 海外代購書籍(需單獨結帳)

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商品描述

Soft computing, as opposed to conventional "hard" computing, tolerates imprecision and uncertainty, in a way very much similar to the human mind. Soft computing techniques include neural networks, evolutionary computation, fuzzy logic, and chaos. The recent years have witnessed tremendous success of these powerful methods in virtually all areas of science and technology, as evidenced by the large numbers of research results published in a variety of journals, conferences, as weil as many excellent books in this book series on Studies in Fuzziness and Soft Computing. This volume is dedicated to recent novel applications of soft computing in communications. The book is organized in four Parts, i.e., (1) neural networks, (2) evolutionary computation, (3) fuzzy logic and neurofuzzy systems, and (4) kernel methods. Artificial neural networks consist of simple processing elements called neurons, which are connected by weights that may be adjusted during learning. Part 1 of the book has seven chapters, demonstrating some of the capabilities of two major types of neural networks, i.e., multiplayer perceptron (MLP) neural networks and Hopfield-type neural networks.

商品描述(中文翻譯)

軟計算(Soft computing)與傳統的「硬」計算相對,能夠容忍不精確和不確定性,這一點與人類思維非常相似。軟計算技術包括神經網絡(neural networks)、進化計算(evolutionary computation)、模糊邏輯(fuzzy logic)和混沌(chaos)。近年來,這些強大方法在幾乎所有科學和技術領域都取得了巨大的成功,這一點可以從各種期刊、會議上發表的大量研究成果以及本系列《模糊性與軟計算研究》(Studies in Fuzziness and Soft Computing)中的許多優秀書籍中得到證明。本卷專注於軟計算在通信領域的最新應用。該書分為四個部分,即:(1)神經網絡,(2)進化計算,(3)模糊邏輯和神經模糊系統(neurofuzzy systems),以及(4)核方法(kernel methods)。人工神經網絡由稱為神經元(neurons)的簡單處理單元組成,這些神經元通過在學習過程中可以調整的權重相連。該書的第一部分包含七章,展示了兩種主要類型的神經網絡的某些能力,即多層感知器(multilayer perceptron, MLP)神經網絡和霍普菲爾德型(Hopfield-type)神經網絡。

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