Statistical Learning Theory
Статистическая теория обучения
1999-11-01
SCID: 54.1/bveavnhq
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function estimationgeneralization theorylearning consistencysmall-sample learningstatistical learning theory
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Abstract (AI)
A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Key Findings
1
Claims broad applicability of the theory to computer science and robotics domains through lucid, comprehensive exposition.
2
Covers estimation of functions from small data pools and applies these estimations to real-life problems.
3
Presents a unified statistical theory of learning and generalization focused on choosing functions from empirical data.
4
Provides a method to determine necessary and sufficient conditions for consistency of learning processes.
Research Object
Statistical theory of learning and generalization (learning processes selecting functions from empirical data)
Research Subject
Conditions for consistency and generalization of learning processes, including function estimation from small data samples and applicability to real-world problems
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1999-11-01
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