3 edition of **Computational Learning Theory** found in the catalog.

- 42 Want to read
- 19 Currently reading

Published
**August 24, 2001**
by Springer
.

Written in English

- Machine Learning,
- Computers,
- Computers - General Information,
- Computer Books: General,
- Artificial Intelligence - General,
- General,
- Algorithmic Learning,
- Classification,
- Computational Learning,
- Computers / Artificial Intelligence,
- Data Mining,
- Game Theory,
- Inference,
- Q-Learning,
- Computer Science,
- Cybernetics,
- Computational learning theory,
- Congresses

**Edition Notes**

Contributions | David Helmbold (Editor), Bob Williamson (Editor) |

The Physical Object | |
---|---|

Format | Paperback |

Number of Pages | 631 |

ID Numbers | |

Open Library | OL12774865M |

ISBN 10 | 3540423435 |

ISBN 10 | 9783540423430 |

Angluin D Computational learning theory Proceedings of the twenty-fourth annual ACM symposium on Theory of Computing, () Board R and Pitt L On the necessity of Occam algorithms Proceedings of the twenty-second annual ACM symposium on Theory of Computing, () Save to Binder. Create a New Binder. Name. Book: Computational Developmental Psychology: Thomas R. Shultz: Book: Computational Learning Theory and Natural Learning Systems, Volume 3: Thomas Petsche, Stephen José Hanson, Jude Shavlik: Book: Computational Learning Theory and Natural Learning Systems, Volume 2.

and psychologists study learning in animals and humans. In this book we fo-cus on learning in machines. There are several parallels between animal and machine learning. Certainly, many techniques in machine learning derive from the e orts of psychologists to make more precise their theories of animal and human learning through computational Size: 1MB. questions, while the second question is within the purview of computational learning theory [Val84,KV94]. However, there are some overlaps between these two elds. In particular, we can immediately classify learning problems into easy and hard ones by looking at how their sample complexity grows as a function of 1="and 1.

About this book Computational complexity theory has developed rapidly in the past three decades. The list of surprising and fundamental results proved since alone could ﬁll a book: these include new probabilistic deﬁnitions of classical complexity classes (IP = PSPACE and the PCP Theorems). The goal of this series is to explore the intersection of three historically distinct areas of learning research: computational learning theory, neural networks andAI machine learning. Although each field has its own conferences, journals, language, research, results, and directions, there is a growing intersection and effort to bring these.

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Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning.

Each topic in the book has been chosen to elucidate a general principle, which Cited by: 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.

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central. Book Abstract: 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 ational learning theory is a new and rapidly expanding area of research that.

This volume presents the proceedings of the Second European Conference on Computational Learning Theory (EuroCOLT '95), held in Computational Learning Theory book, Spain in March The book contains full versions of the Computational Learning Theory book papers accepted for presentation at the conference as.

Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning.

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/5.

Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered/5(2).

This volume presents the proceedings of the Second European Conference on Computational Learning Theory (EuroCOLT '95), held in Barcelona, Spain in March The book contains full versions of the 28 papers accepted for presentation at the conference as well as three invited papers.

All relevant. Book Title Computational Learning Theory Book Subtitle 14th Annual Conference on Computational Learning Theory, COLT and 5th European Conference on Computational Learning Theory, EuroCOLTAmsterdam, The Netherlands, July, Proceedings Editors. David Helmbold; Bob Williamson; Series Title Lecture Notes in Artificial.

Computational Learning Theory 14th Annual Conference on Computational Learning Theory, COLT and 5th European Conference on Computational Learning Theory, EuroCOLT Amsterdam, The Netherlands, July 16–19, Proceedings.

Download computational learning theory books or read computational learning theory books online books in PDF, EPUB and Mobi Format. Click Download or Read Online button to get computational learning theory books book now.

This site is like a library, Use search box in the widget to get ebook that you want. Note:. The book's writing style is clear and pleasant, reflecting the current trend toward intuitive, philosophical presentations of complex technical matters. Although readers should not expect to find plug-and-play algorithms, the book is recommended to everyone as a solid introduction to the theoretical aspects of computational learning.

If you are in India and are used to Indian methodologies of teaching then go for Theory of Computer Science By KLP Mishra.

Otherwise, Introduction to Automata Theory, Languages and Computation by Hopcroft and Ullman is considered a standard book. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning.

Each topic in the book has been chosen to elucidate a general principle, which. This is a self contained volume in which the authors concentrate on the 'probably approximately correct model'.

It will therefore form an introduction to. 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 ational learning theory is a new and rapidly expanding area of research that examines formal models of.

Avi Wigderson Mathematics and Computation Draft: Ma Acknowledgments In this book I tried to present some of the knowledge and understanding I acquired in my four decades in the eld.

The main source of this knowledge was the Theory of Computation commu-nity, which has been my academic and social home throughout this period. This book constitutes the refereed proceedings of the 14th Annual and 5th European Conferences on Computational Learning Theory, COLT/EuroCOLTheld in Amsterdam, The Netherlands, in July The 40 revised full papers presented together with one invited paper were carefully reviewed and selected from a total of 69 submissions.

Computational Learning Theory Eric Xing The Computational complexity concerns the computation resources applied to the training data to extract from it learner’s predictions. In our study of learning theory, it will be useful to abstract away from the specific. : An Introduction to Computational Learning Theory (The MIT Press) () by Kearns, Michael J.; Vazirani, Umesh and a great selection of similar New, Used and Collectible Books available now at great prices/5(34).

Today, as data explosions and computational power indexing increase, probability theory has played a central role in machine learning. Like linear algebra, probability theory also represents a way of looking at the world, with a focus on the ubiquitous possibilities.Computational learning theory explores the limits of learnability.

Studying language acquisition from this perspective involves identifying classes of languages that are learnable from the available data, within the limits of time and computational resources available to the learner.Computational learning theory is one of the first attempts to construct a mathematical theory of a cognitive process.

It has been a field of much interest and rapid growth in recent years. This text provides a framework for studying a variety of algorithmic processes, such as those currently in use for training artificial neural networks.