The Computational Complexity Of Machine Learning

Download The Computational Complexity Of Machine Learning full books in PDF, epub, and Kindle. Read online free The Computational Complexity Of Machine Learning ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!


Related Books

The Computational Complexity of Machine Learning
Language: en
Pages: 194
Authors: Michael J. Kearns
Categories: Computers
Type: BOOK - Published: 1990 - Publisher: MIT Press

DOWNLOAD EBOOK

We also give algorithms for learning powerful concept classes under the uniform distribution, and give equivalences between natural models of efficient learnabi
Understanding Machine Learning
Language: en
Pages: 415
Authors: Shai Shalev-Shwartz
Categories: Computers
Type: BOOK - Published: 2014-05-19 - Publisher: Cambridge University Press

DOWNLOAD EBOOK

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying thei
Computational Complexity
Language: en
Pages: 609
Authors: Sanjeev Arora
Categories: Computers
Type: BOOK - Published: 2009-04-20 - Publisher: Cambridge University Press

DOWNLOAD EBOOK

New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.
Proceedings of International Scientific Conference on Telecommunications, Computing and Control
Language: en
Pages: 541
Authors: Nikita Voinov
Categories: Technology & Engineering
Type: BOOK - Published: 2021-04-28 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book provides a platform for academics and practitioners for sharing innovative results, approaches, developments, and research projects in computer scienc
An Introduction to Computational Learning Theory
Language: en
Pages: 230
Authors: Michael J. Kearns
Categories: Computers
Type: BOOK - Published: 1994-08-15 - Publisher: MIT Press

DOWNLOAD EBOOK

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for rese