Articles liés à Fundamentals of Predictive Text Mining
Synopsis
This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.
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À propos de l?auteur
Dr. Sholom M. Weiss is a Professor Emeritus of Computer Science at Rutgers University, a Fellow of the Association for the Advancement of Artificial Intelligence, and co-founder of AI Data-Miner LLC, New York.
Dr. Nitin Indurkhya is faculty member at the School of Computer Science and Engineering, University of New South Wales, Australia, and the Institute of Statistical Education, Arlington, VA, USA. He is also a co-founder of AI Data-Miner LLC, New York.
Dr. Tong Zhang is a Professor of Statistics and Biostatistics at Rutgers University.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Fundamentals of Predictive Text Mining
Edité par
Springer London Sep 2015, 2015
ISBN 10 : 144716749X
ISBN 13 : 9781447167495
Neuf
Couverture rigide
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software. 256 pp. Englisch. N° de réf. du vendeur 9781447167495
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