Isbn: 9783319242095 - unsupervised learning algorithms (13 resultados)

Hardcover. Condición: New. As New. Pages are clean and unmarked. Boards are clean and square. Binding is tight. Text block is clean.

Condición: New. In English.

Condición: New. pp. 290.

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Hardcover. Condición: Brand New. 558 pages. French language. 9.50x6.50x1.50 inches. In Stock.

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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how withthe proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest includeanomaly detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field. 568 pp. Englisch.…

Condición: new. Questo è un articolo print on demand.

Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Contains the state-of-the-art in unsupervised learning in a single comprehensive volumeFeatures numerous step-by-step tutorials help the reader to learn quicklyIncludes several tips on how to protect flash sites from hackers and a special c.…

Condición: New. Print on Demand pp. 290.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest includeanomaly detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 568 pp. Englisch.…

Condición: New. PRINT ON DEMAND pp. 290.

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how withthe proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest includeanomaly detection, clustering, feature extraction, and applications of unsupervised learning. Each chapter is contributed by a leading expert in the field.…