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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
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.
Siemens Aktiengesellschaft Patent Landscape Analysis – January 1, 1994 to December 31, 2013
The following analysis illustrates the underlying trends and relationships of U.S. issued patents of the subject company. The analysis employs two frequently used patent classification methods: US Patent Classification (UPC) and International Patent Classification (IPC). Aside from assisting patent examiners in determining the field of search for newly submitted patent applications, the two classification methods play a pivotal role in the characterization and analysis of technologies contained in collections of patent data. The analysis also includes the company’s most prolific inventors, top cited patents as well as foreign filings by technology area.
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.
Tenth Scandinavian Conference on Artificial Intelligence
The Scandinavian Conference on Artificial Intelligence is one of the regional AI conferences in Europe. This title includes contributions that exemplify the diversity of research in artificial intelligence and confirm the achievement and magnitude of 25 years AI research in Scandinavia.
Proceedings of the Fourth SIAM International Conference on Data Mining
The Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked data, and advances in Bayesian inference techniques. This proceedings includes 61 research papers.
Machine Learning: ECML 2007
This book constitutes the refereed proceedings of the 18th European Conference on Machine Learning, ECML 2007, held in Warsaw, Poland, September 2007, jointly with PKDD 2007. The 41 revised full papers and 37 revised short papers presented together with abstracts of four invited talks were carefully reviewed and selected from 592 abstracts submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.
Introduction to Semi-Supervised Learning
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mi...
