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Advances in large margin classifiers
The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a …
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- ISBN/ISSN
- 9780262283977
- Collation
- 1 online resource (vi, 412 pages) :illustrations.
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Predicting structured data
Collected papers based on talks presented at two Neural Information Processing Systems workshops.State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must sati…
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- ISBN/ISSN
- 9780262255691
- Collation
- 1 online resource (viii, 348 pages) :illustrations.
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Learning with kernels :support vector machines, regularization, optimization,…
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by…
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- ISBN/ISSN
- 9780262256933
- Collation
- 1 online resource (xviii, 626 pages) :illustrations.
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Kernel methods in computational biology
"A Bradford book."Modern machine learning techniques are proving to be extremely valuable for the analysis of data in computational biology problems. One branch of machine learning, kernel methods, lends itself particularly well to the difficult aspects of biological data, which include high dimensionality (as in microarray measurements), representation as discrete and structured data (as in DN…
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- ISBN/ISSN
- 0262256924
- Collation
- 1 online resource (ix, 400 pages) :illustrations.
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