Statistical spoken language understanding systems - Info and Reading Options
By Amparo Albalate

"Statistical spoken language understanding systems" was published by ISTE in 2011 - London, it has 244 pages and the language of the book is English.
“Statistical spoken language understanding systems” Metadata:
- Title: ➤ Statistical spoken language understanding systems
- Author: Amparo Albalate
- Language: English
- Number of Pages: 244
- Publisher: ISTE
- Publish Date: 2011
- Publish Location: London
“Statistical spoken language understanding systems” Subjects and Themes:
- Subjects: ➤ Statistical methods - TECHNOLOGY & ENGINEERING / Electronics / General - Discourse analysis - Data mining - Speech processing systems - Computational intelligence - Machine learning - Computational learning theory - Supervised learning (Machine learning)
Edition Specifications:
- Pagination: p. cm.
Edition Identifiers:
- The Open Library ID: OL25001469M - OL16113727W
- Online Computer Library Center (OCLC) ID: 700509842 - 857717622
- Library of Congress Control Number (LCCN): 2010040730
- ISBN-13: 9781848212039
- All ISBNs: 9781848212039
AI-generated Review of “Statistical spoken language understanding systems”:
"Statistical spoken language understanding systems" Description:
The Open Library:
"This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described"--
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