We specialize in hard to find items
All Categories
Fundamentals of Predictive Text Mining (Texts in Computer Science)
Share Tweet
*Price and Stocks may change without prior notice
*Packaging of actual item may differ from photo shown
- Electrical items MAY be 110 volts.
- 7 Day Return Policy
- All products are genuine and original
About Fundamentals Of Predictive Text Mining
Product Description 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. Review “Fundamentals of predictive text mining is a second edition that is designed as a textbook, with questions and exercises in each chapter. … The book can be used with data mining software for hands-on experience for students. … The book will be very useful for people planning to go into this field or to learn techniques that could be used in a big data environment.” (S. Srinivasan, Computing Reviews, February, 2016) From the Back Cover 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, and 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. Topics and features: Presents a comprehensive, practical and easy-to-read introduction to text mining Includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter Explores the application and utility of each method, as well as the optimum techniques for specific scenarios Provides several descriptive case studies that take readers from problem description to systems deployment in the real world Describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English) Contains links to free downloadable industrial-quality text-mining software and other supplementary instruction material Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students. About the Author 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.
