Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	September 2015
Genre:	EDV / Informatik
	Application software / B / Computer and Information Systems Applications / Computer Appl. in Administrative Data Processing / computer science / Data Mining / Data Mining and Knowledge Discovery / Data Warehousing / Database Management / database programming / Databases / Expert systems / knowledge-based systems / Information Retrieval / Information Storage and Retrieval / Information technology# general issues / Natural language & machine translation / Natural language processing (Computer science) / Natural Language Processing (NLP) / Public Administration mehr...
ISBN:	9781447167495
EAN-Code:	9781447167495
Verlag:	Springer EN
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D 20 mm
Gewicht:	5089 gr
Seiten:	239
Illustration:	XIII, 239 p. 115 illus., schwarz-weiss Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.