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“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)
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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- VerlagSpringer
- Erscheinungsdatum2015
- ISBN 10 144716749X
- ISBN 13 9781447167495
- EinbandTapa dura
- Auflage2
- Anzahl der Seiten256
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Buchbeschreibung Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Artikel-Nr. 9781447167495
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