Filter by
Advanced Structured Prediction
The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. …
- Edition
- -
- ISBN/ISSN
- 0262322951
- Collation
- 1 online resource (ix, 415 pages) :illustrations.
- Series Title
- -
- Call Number
- -
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…
- Edition
- -
- ISBN/ISSN
- 9780262255691
- Collation
- 1 online resource (viii, 348 pages) :illustrations.
- Series Title
- -
- Call Number
- -
Automatic algorithm recognition and replacement :a new approach to program op…
"Parallel computation will become the norm in the coming decades. Unfortunately, advances in parallel hardware have far outpaced parallel applications of software. There are currently two approaches to applying parallelism to applications. One is to write completely new applications in new languages. But abandoning applications that work is unacceptable to most nonacademic users of high-perform…
- Edition
- -
- ISBN/ISSN
- 058533336X
- Collation
- 1 online resource (viii, 219 pages)
- Series Title
- -
- Call Number
- -
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…
- Edition
- -
- ISBN/ISSN
- 9780262256933
- Collation
- 1 online resource (xviii, 626 pages) :illustrations.
- Series Title
- -
- Call Number
- -
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…
- Edition
- -
- ISBN/ISSN
- 9780262256339
- Collation
- 1 online resource (xx, 364 pages) :illustrations.
- Series Title
- -
- Call Number
- -
Learning in embedded systems
"A Bradford book."It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases.Learning to perform complex action strategies is an important problem in the fields of artif…
- Edition
- -
- ISBN/ISSN
- 0262288508
- Collation
- 1 online resource (xi, 176 pages) :illustrations
- Series Title
- -
- Call Number
- -
The rapid evaluation of potential fields in particle systems
"Leslie Greengard received his doctorate from Yale University where he is a NSF Postdoctoral Fellow in the Computer Science Department. The Rapid Evaluation of Potential Fields in Particle Systems is a 1987 ACM Distinguished Dissertation.""The Rapid Evaluation of Potential Fields in Particle Systems presents a group of algorithms for the computation of the potential and force fields in large-sc…
- Edition
- -
- ISBN/ISSN
- 9780262256254
- Collation
- 1 online resource (iv, 90 pages) :illustrations.
- Series Title
- -
- Call Number
- -
Self-stabilization
OCLC-licensed vendor bibliographic record.
- Edition
- -
- ISBN/ISSN
- 058523986X
- Collation
- 1 online resource (197 pages) :illustrations
- Series Title
- -
- Call Number
- -
Computer Science, Information & General Works 
Philosophy & Psychology 
Religion 
Social Sciences 
Language 
Pure Science 
Applied Sciences 
Art & Recreation 
Literature 
History & Geography