A Reader's Guide to the Social Sciences / Edited by Bert F. Hoselitz ; with chapters by Peter M. Blau...[et al.].

• Record Nr.: UNINA990003176490403321
• Pubbl/distr/stampa: New York : Free Press, 1970
• Edizione: [Revised ed.]
• Descrizione fisica: XIV, 425 p. ; 24 cm
• Disciplina: 10100
• Locazione: SES
• Collocazione: 10100 REA/70
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Machine learning applications in software engineering [[electronic resource] /] / editors, Du Zhang, Jeffrey J.P. Tsai

• Record Nr.: UNINA9910450220803321
• Pubbl/distr/stampa: Hackensack, N.J. ; ; Hong Kong, : World Scientific, c2005
• ISBN: 1-281-37255-2; 9786611372552; 981-256-927-8
• Descrizione fisica: 1 online resource (367 p.)
• Collana: Series on software engineering and knowledge engineering ; ; v. 16
• Altri autori (Persone): ZhangDu; TsaiJeffrey J.-P
• Disciplina: 006.31
• Soggetti: Software engineering; Computer software - Quality control; Computer software - Evaluation; Machine learning; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. 345-355) and index.
• Nota di contenuto: ACKNOWLEDGMENT; TABLE OF CONTENTS; Chapter 1 Introduction to Machine Learning and Software Engineering; Chapter 2 ML Applications in Prediction and Estimation; Chapter 3 ML Applications in Property and Model Discovery; Chapter 4 ML Applications in Transformation; Chapter 5 ML Applications in Generation and Synthesis; Chapter 6 ML Applications in Reuse; Chapter 7 ML Applications in Requirement Acquisition; Chapter 8 ML Applications in Management of Development Knowledge; Chapter 9 Guidelines and Conclusion; References
• Sommario/riassunto: Machine learning deals with the issue of how to build computerprograms that improve their performance at some tasks throughexperience. Machine learning algorithms have proven to be of greatpractical value in a variety of application domains. Not surprisingly,the field of software engineering turns out to be a fertile groundwhere many software development and maintenance tasks could beformulated as learning problems and approached in terms of learningalgorithms.