Journal Article10.1002/ARIS.1440380107
Web mining: Machine learning for web applications
Hsinchun Chen,Michael Chau +1 more
123
TL;DR: La fouille du Web est entendue comme la decouverte and l'analyse d'information utile sur le Web, domaine d'etudes a la croisee of the recherche d’information, therecherche surLe Web, l'apprentissage automatique, les bases de donnees, the fouille de donnes and la fouille of texte.
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Abstract: L'A. examine la recherche sur l'apprentissage automatique et les techniques de recherche d'information traditionnelles, et leurs possibles applications pour les systemes de fouille sur le Web. La fouille du Web est entendue comme la decouverte et l'analyse d'information utile sur le Web, domaine d'etudes a la croisee de la recherche d'information, la recherche sur le Web, l'apprentissage automatique, les bases de donnees, la fouille de donnees et la fouille de texte. Les etudes sur la fouille du Web se repartissent en trois categories, selon que l'on considere le contenu du Web, sa structure ou son usage.
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Recognizing contributions in wikis: Authorship categories, algorithms, and visualizations
Ofer Arazy,Eleni Stroulia,Stan Ruecker,Cristina Arias,Carlos Fiorentino,Veselin Ganev,Timothy Yau +6 more
TL;DR: It is demonstrated that the proposed automated techniques can estimate fairly accurately the quantity of editors' contributions across various authorship categories, and that the visualizations introduced can clearly convey this information to users.
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Machine Learning: Algorithms and Applications
Mohssen Mohammed,Muhammad Badruddin Khan,Eihab Bashier Mohammed Bashier +2 more
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TL;DR: The authors provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.
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Patent
Interactive machine learning advice facility
Thomas Pinckney,Christopher John Dixon,Matthew Ryan Gattis +2 more
- 31 Oct 2008
TL;DR: In this article, improved capabilities are described for helping a user make a decision through the use of a machine learning facility, such as a recommendation, a diagnosis, a conclusion, advice, and the like.
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Knowledge Management, Data Mining, and Text Mining in Medical Informatics
Hsinchun Chen,Sherrilynne S. Fuller,Carol Friedman,William R. Hersh +3 more
- 01 Jan 2005
TL;DR: This chapter introduces five major paradigms for machine learning and data analysis including: probabilistic and statistical models, symbolic learning and rule induction, neural networks, evolution-based algorithms, and analytic learning and fuzzy logic, and discusses their relevance and potential for biomedical research.
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Sports Data Mining
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TL;DR: Chen et al. as mentioned in this paper present the state-of-the-art in sports data mining, focusing on five sports: baseball, football, basketball, soccer, and greyhound racing.
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