Cost-Sensitive Machine Learning (eBook, PDF)

Redaktion: Krishnapuram, Balaji; Rao, R. Bharat; Yu, Shipeng

• Format: PDF

Produktbeschreibung

In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/attributes for test dataCost of user feedback collect

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 331
• Erscheinungstermin: 19. Dezember 2011
• Englisch
• ISBN-13: 9781439839287
• Artikelnr.: 57533498

Autorenporträt

Balaji Krishnapuram is a senior R&D 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. Shipeng Yu is a senior staff scientist at Siemens Medical Solutions. He earned a Ph.D. in computer science from the University of Munich. His research interests include statistical machine learning, data mining, Bayesian analysis, information retrieval and extraction, healthcare analytics, and personalized medicine. R. Bharat Rao is senior director and head of Knowledge Solutions at Siemens Medical Solutions, where was recognized as one of its Inventors of the Year in 2005. He also received the 2011 ACM SIGKDD Lifetime Service Award for pioneering applications of data mining for healthcare. He earned a Ph.D. in electrical and computer engineering from the University of Illinois at Urbana-Champaign. His research interests include machine learning, healthcare analytics, mining large data, and personalized medicine.

Inhaltsangabe

THEORECTICAL UNDERPINNINGS OF COST
SENSTIVE MACHINE LEARNING: Algorithms for Active Learning. Semi
Supervised Learning: Some Recent Advances. Transfer Learning, Multi
Task Learning, and Cost
Sensitive Learning. Cost
Sensitive Cascades. Selective Data Acquisition for Machine Learning. COST
SENSITIVE MACHINE LEARNING APPLICATIONS: Minimizing Annotation Costs in Visual Category Learning. Reliability and Redundancy: Reducing Error Cost in Medical Imaging. Cost
Sensitive Learning in Computational Advertising. Cost
Sensitive Machine Learning for Information Retrieval. Index.