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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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21#
發(fā)表于 2025-3-25 04:56:42 | 只看該作者
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發(fā)表于 2025-3-25 08:49:58 | 只看該作者
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發(fā)表于 2025-3-25 14:17:21 | 只看該作者
Inductive Inference for Large Scale Text Classification978-3-642-04533-2Series ISSN 1860-949X Series E-ISSN 1860-9503
24#
發(fā)表于 2025-3-25 19:19:49 | 只看該作者
Kernel Machines for Text ClassificationThis chapter details the concept of kernel methods and presents the foundations of two paradigmatic techniques: support vector machines and relevance vector machines. Both are introduced in a text classification perspective and then results and comparisons of their application to benchmark corpora are presented.
25#
發(fā)表于 2025-3-25 22:46:17 | 只看該作者
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發(fā)表于 2025-3-26 00:22:05 | 只看該作者
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發(fā)表于 2025-3-26 05:40:27 | 只看該作者
Framework for Text Classificationcus on the particular developments in kernel methods triggered by new problems in text mining and on how to extract useful knowledge by mining relationships between data. We include a few promising research directions that are likely to expand in the future.
28#
發(fā)表于 2025-3-26 11:26:11 | 只看該作者
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發(fā)表于 2025-3-26 15:51:50 | 只看該作者
30#
發(fā)表于 2025-3-26 19:49:10 | 只看該作者
1860-949X ts 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
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