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Titlebook: Machine Learning Paradigms; Applications in Reco Aristomenis S. Lampropoulos,George A. Tsihrintzis Book 2015 Springer International Publish

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發(fā)表于 2025-3-21 18:20:22 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Machine Learning Paradigms
副標題Applications in Reco
編輯Aristomenis S. Lampropoulos,George A. Tsihrintzis
視頻videohttp://file.papertrans.cn/621/620413/620413.mp4
概述Presents recent applications of Recommender Systems.Intended for both the expert and researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the gener
叢書名稱Intelligent Systems Reference Library
圖書封面Titlebook: Machine Learning Paradigms; Applications in Reco Aristomenis S. Lampropoulos,George A. Tsihrintzis Book 2015 Springer International Publish
描述.This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems..The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and
出版日期Book 2015
關(guān)鍵詞Class Imbalance; Intelligent Systems; Machine Learning; One-class Classification; Pattern Recognition; Pe
版次1
doihttps://doi.org/10.1007/978-3-319-19135-5
isbn_softcover978-3-319-38496-2
isbn_ebook978-3-319-19135-5Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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發(fā)表于 2025-3-21 23:19:06 | 只看該作者
Review of Previous Work Related to Recommender Systems,is chapter reviews the state of the art of the main approaches to designing RSs that address the problems caused by information overload. In general, the methods implemented in a RS fall within one of the following categories: (a) Content-based Methods, (b) Collaborative Methods and (c) Hybrid Metho
板凳
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The Learning Problem,s distinctive attributes of intelligent behavior. Machine Learning is the study of how to develop algorithms, computer applications, and systems that have the ability to learn and, thus, improve through experience their performance at some tasks. This chapter presents the formalization of the Machin
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Similarity Measures for Recommendations Based on Objective Feature Subset Selection, users are constructed from . audio signal features by associating different music similarity measures to different users. Specifically, our approach in developing MUSIPER is based on investigating certain subsets in the . feature set and their relation to the . music similarity perception of indivi
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Cascade Recommendation Methods,n step involves the incorporation of a one-class classifier which is trained exclusively on positive patterns. The one-class learning component of the first-level serves the purpose of recognizing instances from the class of desirable patterns as opposed to non-desirable patterns. On the other hand,
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Conclusions and Future Work,verwhelmed by huge amounts of information that, in the absence of RS, they should browse or examine. In this book, we presented a number of innovative RS, which are summarized in this chapter. Conclusions are drawn and avenues of future research are identified.
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tionalist”. But what does this exactly mean? Is he a “rationalist” in the same sense in Mathematics and Politics, in Physics and Jurisprudence, in Metaphysics and Theology, in Logic and Linguistics, in Technology and Medicine, in Epistemology and Ethics? What are the most significant features of his
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