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Titlebook: Machine Learning: ECML-98; 10th European Confer Claire Nédellec,Céline Rouveirol Conference proceedings 1998 Springer-Verlag Berlin Heidelb

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發(fā)表于 2025-3-21 19:39:23 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Machine Learning: ECML-98
副標題10th European Confer
編輯Claire Nédellec,Céline Rouveirol
視頻videohttp://file.papertrans.cn/621/620760/620760.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning: ECML-98; 10th European Confer Claire Nédellec,Céline Rouveirol Conference proceedings 1998 Springer-Verlag Berlin Heidelb
描述This book constitutes the refereed proceedings of the 10th European Conference on Machine Learning, ECML-98, held in Chemnitz, Germany, in April 1998..The book presents 21 revised full papers and 25 short papers reporting on work in progress together with two invited contributions; the papers were selected from a total of 100 submissions. The book is divided in sections on applications of ML, Bayesian networks, feature selection, decision trees, support vector learning, multiple models for classification, inductive logic programming, relational learning, instance-based learning, clustering, genetic algorithms, reinforcement learning and neural networks.
出版日期Conference proceedings 1998
關(guān)鍵詞Bayesian network; Boosting; Support Vector Machine; Text; algorithms; machine learning; programming; reinfo
版次1
doihttps://doi.org/10.1007/BFb0026664
isbn_softcover978-3-540-64417-0
isbn_ebook978-3-540-69781-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 1998
The information of publication is updating

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Sylvain Létourneaue,Stan Matwin,Fazel Families true WLAN experience nowadays, imagine a world without it? This book dealswith the design oflinearCMOS RF PowerAmpli?ers(PAs). The RF PA is a very important part of the RF transceiver, the device that enables wireless communications. Two important aspects that are key to keep the advances in RF PA
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Sa?o D?eroskil,Nico Jacobs,Martin Molina,Carlos Mourea direct approach based on a deterministic algorithm. The present chapter is concerned with these approaches. Typically, these algorithms are based on linear transformations on a set of intensity measurements of the signal. These intensity measurements are performed at different planes as the signal
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Dunja Mladeni?ins a further ‘Sources and Comments’ section.The only prerequisites are a familiarity with metric spaces, the basic theory of Hilbert and Banach spaces and fundamentals of complex analysis. More advanced tools, only needed occasionally, are provided in two appendices. A self-contained exposition, th
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Huan Liul,Hiroshi Motoda,Manoranjan Dashins a further ‘Sources and Comments’ section.The only prerequisites are a familiarity with metric spaces, the basic theory of Hilbert and Banach spaces and fundamentals of complex analysis. More advanced tools, only needed occasionally, are provided in two appendices. A self-contained exposition, th
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Rui Camacholins a further ‘Sources and Comments’ section.The only prerequisites are a familiarity with metric spaces, the basic theory of Hilbert and Banach spaces and fundamentals of complex analysis. More advanced tools, only needed occasionally, are provided in two appendices. A self-contained exposition, th
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Hilan Bensusanins a further ‘Sources and Comments’ section.The only prerequisites are a familiarity with metric spaces, the basic theory of Hilbert and Banach spaces and fundamentals of complex analysis. More advanced tools, only needed occasionally, are provided in two appendices. A self-contained exposition, th
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