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Nearest-neighbor methods in learning and vision :theory and practice
" ... held in Whistler, British Columbia ... annual conference on Neural Information Processing Systems (NIPS) in December 2003"--Preface.Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, ma…
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- ISBN/ISSN
- 9780262256957
- Collation
- 1 online resource (vi, 252 pages) :illustrations.
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Algorithms
In the tradition of Real World Algorithms: A Beginner's Guide, Panos Louridas is back to introduce algorithms in an accessible manner, utilizing various examples to explain not just what algorithms are but how they work. Digital technology runs on algorithms, sets of instructions that describe how to do something efficiently. Application areas range from search engines to tournament scheduling,…
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- ISBN/ISSN
- 9780262358675
- Collation
- 1 online resource
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Learning Kernel Classifiers: Theory and Algorithms
Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This…
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- 1
- ISBN/ISSN
- 9780262256339
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Online stochastic combinatorial optimization
"Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. S…
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- ISBN/ISSN
- 9780262257152
- Collation
- 1 online resource (xiii, 232 pages) :illustrations
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Computational Molecular Biology: An Algorithmic Approach
In one of the first major texts in the emerging field of computational molecular biology, Pavel Pevzner covers a broad range of algorithmic and combinatorial topics and shows how they are connected to molecular biology and to biotechnology. The book has a substantial "computational biology without formulas" component that presents the biological and computational ideas in a relatively simple ma…
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- ISBN/ISSN
- 9780262281522
- Collation
- 1 online resource (xviii, 314 pages).
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Parallel Algorithms for Regular Architectures :Meshes and Pyramids.
AnnotationOCLC-licensed vendor bibliographic record.
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- ISBN/ISSN
- 9780262279833
- Collation
- 1 online resource (336 pages)
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Introduction to statistical relational learning
"Index" : an online index is available on the book webpage at http://www.cs.umd.edu/srl-book/index.htm.Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications.OCLC-licensed vendor bibliographic record.
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- ISBN/ISSN
- 9780262256230
- Collation
- 1 online resource (ix, 586 pages) :illustrations.
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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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Live coding :a user's manual
"A multi-authored comprehensive introduction to live coding's potential open up deeper questions about contemporary cultural production and computational culture"--OCLC-licensed vendor bibliographic record.
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- ISBN/ISSN
- 9780262372633
- Collation
- 1 online resource.
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An introduction to computational learning theory
Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics.Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with th…
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- ISBN/ISSN
- 0585350531
- Collation
- 1 online resource (xii, 207 pages) :illustrations
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- Call Number
- 005 KEA i
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