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Cost-Sensitive Machine Learning
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
Machine Learning
A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students
Biocomputing 2004 - Proceedings Of The Pacific Symposium
The Pacific Symposium on Biocomputing (PSB 2004) is an international, multidisciplinary conference for the presentation and discussion of current research on the theory and application of computational methods in problems of biological significance. The rigorously peer-reviewed papers and presentations are collected in this archival proceedings volume.PSB is a forum for the presentation of work on databases, algorithms, interfaces, visualization, modeling and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. PSB 2004 brings together top researchers from the US, the Asia-Pacific region and the rest of the world to exchange research findings and address open issues in all aspects of computational biology.The proceedings have been selected for coverage in:
Building Success with Business Ethics
A brand new collection of powerful insights into ethical and effective business leadership… 4 pioneering books, now in a convenient e-format, at a great price! 4 remarkable eBooks help you lead more successfully by leading more ethically Honor, ethics, and compassion are central to effective leadership. Now, an extraordinary new eBook collection reveals why this is true, and how you can lead more honorably and successfully in your own organization. In Winners Never Cheat: Even in Difficult Times, New and Expanded Edition, Jon M. Huntsman shows how to succeed at the top, without sacrificing the principles that make life worth living. Huntsman personally built a $12 billion company from scra...
Bayesian Programming
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in natur
Digital Mammography
This book constitutes the refereed proceedings of the 8th International Workshop on Digital Mammography, IWDM 2006, held in Manchester, UK, June 2006. The book presents 52 revised full papers and 34 revised poster papers, organized in topical sections on breast density, CAD, clinical practice, tomosynthesis, registration and multiple view mammmography, physics models, wavelet methods, full-field digital mammography, and segmentation.
Pacific Symposium on Biocomputing 2004
The Pacific Symposium on Biocomputing (PSB 2004) is an international, multidisciplinary conference for the presentation and discussion of current research on the theory and application of computational methods in problems of biological significance. The rigorously peer-reviewed papers and presentations are collected in this archival proceedings volume. PSB is a forum for the presentation of work on databases, algorithms, interfaces, visualization, modeling and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology. PSB 2004 brings together top researchers from the US, the Asia-Pacific region and the rest of the world to exchange research findings and address open issues in all aspects of computational biology. The proceedings have been selected for coverage in: . OCo Biochemistry & Biophysics Citation IndexOao. OCo Index to Scientific & Technical Proceedings- (ISTP- / ISI Proceedings). OCo Index to Scientific & Technical Proceedings (ISTP CDROM version / ISI Proceedings)."
Digital Mammography
This volume (5116) of Springer’s Lecture Notes in Computer Science contains the th proceedings of the 9 International Workshop on Digital Mammography (IWDM) which was held July 20 – 23, 2008 in Tucson, AZ in the USA. The IWDM meetings traditionally bring together a diverse set of researchers (physicists, mathematicians, computer scientists, engineers), clinicians (radiologists, surgeons) and representatives of industry, who are jointly committed to developing technologies to support clinicians in the early detection and subsequent patient management of breast cancer. The IWDM conference series was initiated at a 1993 meeting of the SPIE Medical Imaging Symposium in San Jose, CA, with sub...
Advances in Neural Information Processing Systems 19
The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.
Ensemble Methods
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.
