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Titlebook: Inductive Inference for Large Scale Text Classification; Kernel Approaches an Catarina Silva,Bernardete Ribeiro Book 2010 Springer-Verlag B

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發(fā)表于 2025-3-21 19:22:22 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Inductive Inference for Large Scale Text Classification
副標題Kernel Approaches an
編輯Catarina Silva,Bernardete Ribeiro
視頻videohttp://file.papertrans.cn/464/463884/463884.mp4
概述Presents recent research in inductive inference for Large Scale Text Classification
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Inductive Inference for Large Scale Text Classification; Kernel Approaches an Catarina Silva,Bernardete Ribeiro Book 2010 Springer-Verlag B
描述.Text classification is becoming a crucial task to analysts in different areas. In the last few decades, the production of textual documents in digital form has increased exponentially. Their applications range from web pages to scientific documents, including emails, news and books. Despite the widespread use of digital texts, handling them is inherently difficult - the large amount of data necessary to represent them and the subjectivity of classification complicate matters...This book gives a concise view on how to use kernel approaches for inductive inference in large scale text classification; it presents a series of new techniques to enhance, scale and distribute text classification tasks. It is not intended to be a comprehensive survey of the state-of-the-art of the whole field of text classification. Its purpose is less ambitious and more practical: to explain and illustrate some of the important methods used in this field, in particular kernel approaches and techniques..
出版日期Book 2010
關(guān)鍵詞Kernel Approach; Text Classification; classification; computational intelligence; intelligence; kernel
版次1
doihttps://doi.org/10.1007/978-3-642-04533-2
isbn_softcover978-3-642-26134-3
isbn_ebook978-3-642-04533-2Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2010
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Enhancing SVMs for Text Classificationlearning techniques that integrate knowledge in the classification task to improve the performance of support vector machines (SVMs) in text classification applications..The introduction of unlabeled data in the learning stage is investigated. With the deluge of digital text data, unlabeled texts ar
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Scaling RVMs for Text Classificationattention in this chapter to relevance vector machines (RVMs) and their application to text classification. RVMs’ probabilistic Bayesian nature allows both predictive distributions on testing instances and model-based selection that yields a parsimonious solution. However, scaling up the algorithm i
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Framework for Text Classificationhe common underlying thread has been the integration of knowledge in the inference of inductive learning models without penalizing processing time. This chapter unifies the main topics of this book into a framework. An inductive inference-based text classification framework will provide basic generi
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