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[Free]Graphical Models,Second Edition.Wiley.2009
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Graphical Models_Representations for Learning, Reasoning and Data Mining,Second Edition 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，请与发贴人联系，收到通知后我们将立即删除此帖！ Authors(Editors): Christian Borgelt Matthias Steinbrecher Rudolf Kruse Publisher: Wiley Pub Date: 2009 Pages: 397 ISBN: 978-0-470-72210-7 Preface Although the origins of graphical models can be traced back to the beginning of the 20th century, they have become truly popular only since the mideighties, when several researchers started to use Bayesian networks in expert systems. But as soon as this start was made, the interest in graphical models grew rapidly and is still growing to this day. The reason is that graphical models, due to their explicit and sound treatment of (conditional) dependences and independences, proved to be clearly superior to naive approaches like certainty factors attached to if-then-rules, which had been tried earlier. Data Mining, also called Knowledge Discovery in Databases, is a another relatively young area of research, which has emerged in response to the flood of data we are faced with nowadays. It has taken up the challenge to develop techniques that can help humans discover useful patterns in their data. In industrial applications patterns found with these methods can often be exploited to improve products and processes and to increase turnover. This book is positioned at the boundary between these two highly important research areas, because it focuses on learning graphical models from data, thus exploiting the recognized advantages of graphical models for learning and data analysis. Its special feature is that it is not restricted to probabilistic models like Bayesian and Markov networks. It also explores relational graphical models, which provide excellent didactical means to explain the ideas underlying graphical models. In addition, possibilistic graphical models are studied, which are worth considering if the data to analyze contains imprecise information in the form of sets of alternatives instead of unique values. Looking back, this book has become longer than originally intended. However, although it is true that, as C.F. von Weizs¨acker remarked in a lecture, anything ultimately understood can be said briefly, it is also evident that anything said too briefly is likely to be incomprehensible to anyone who has not yet understood completely. Since our main aim was comprehensibility, we hope that a reader is remunerated for the length of this book by an exposition that is clear and self-contained and thus easy to read. Christian Borgelt, Matthias Steinbrecher, Rudolf Kruse Oviedo and Magdeburg, March 2009 本资源链接友情奉送，共2个可选网络硬盘链接，1.95 MB，免积分资源不提供保质。 -------------------------------------------------------------------------------------------------------- |
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