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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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