Cost sensitive machine learning (8 Ergebnisse)

Zustand: New.

Zustand: New. In English.

Zustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.…

Zustand: Good. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 331 | Sprache: Englisch | Produktart: Bücher | This volume is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications, such as computer-aided medical diagnosis, that effectively trade off different types of costs.…

Paperback. Zustand: Brand New. reprint edition. 313 pages. 9.50x6.50x1.00 inches. In Stock.

Zustand: New. Balaji Krishnapuram is a senior R&ampD manager at Siemens Medical Solutions. He earned a Ph.D. in electrical and computer engineering from Duke University. His research interests include statistical data mining and information retrieval.

Buch. Zustand: Neu. Neuware - The first comprehensive source on cost-sensitive learning, this reference focuses on various research domains in machine learning that attempt to solve cost-sensitive learning with simplifying assumptions. The book explicitly deals with tradeoffs between the various costs and benefits inherent in many domains of machine learning research, including semisupervised learning, active learning, active feature acquisition, and inductive transfer learning. It covers recently developed methods, along with several applications of cost-sensitive learning, including natural language processing, computer vision, medical applications, web ads, and marketing. Real-world applications aid with reader understanding.…