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The Computational Complexity of Machine Learning (Acm Distinguished Dissertation 1989)

Michael J. Kearns

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Description:"The Computational Complexity of Machine Learning" is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for learning.Theorems are presented that help elucidate the boundary of what is efficiently learnable from examples. These results take the form of both algorithms with proofs of their performance, and hardness results demonstrating the intractability of learning in certain natural settings. In addition the book contains lower bounds on the resources required for learning, an extensive study of learning in the presence of errors in the sample data, and several theorems demonstrating reducibilities between learning problems.Michael J. Kearns is Postdoctoral Associate in the Laboratory for Computer Science at MIT.Contents:Definitions, Notations, and Motivation.Overview of Recent Research in Computational Learning Theory.Useful Tools for Distribution-Free Learning.Learning in the Presence of Errors.Lower Bounds on Sample Complexity.Cryptographic Limitations on Polynomial-Time Learning.Distribution-Specific Learning in Polynomial Time.Equivalence of Weak Learning and Group Learning.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with The Computational Complexity of Machine Learning (Acm Distinguished Dissertation 1989). To get started finding The Computational Complexity of Machine Learning (Acm Distinguished Dissertation 1989), you are right to find our website which has a comprehensive collection of manuals listed.
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ISBN: 0262111527

Description: "The Computational Complexity of Machine Learning" is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for learning.Theorems are presented that help elucidate the boundary of what is efficiently learnable from examples. These results take the form of both algorithms with proofs of their performance, and hardness results demonstrating the intractability of learning in certain natural settings. In addition the book contains lower bounds on the resources required for learning, an extensive study of learning in the presence of errors in the sample data, and several theorems demonstrating reducibilities between learning problems.Michael J. Kearns is Postdoctoral Associate in the Laboratory for Computer Science at MIT.Contents:Definitions, Notations, and Motivation.Overview of Recent Research in Computational Learning Theory.Useful Tools for Distribution-Free Learning.Learning in the Presence of Errors.Lower Bounds on Sample Complexity.Cryptographic Limitations on Polynomial-Time Learning.Distribution-Specific Learning in Polynomial Time.Equivalence of Weak Learning and Group Learning.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with The Computational Complexity of Machine Learning (Acm Distinguished Dissertation 1989). To get started finding The Computational Complexity of Machine Learning (Acm Distinguished Dissertation 1989), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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The Computational Complexity of Machine Learning (Acm Distinguished Dissertation 1989)

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