Understanding Machine Learning PDF Download

Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Understanding Machine Learning PDF full book. Access full book title Understanding Machine Learning by Shai Shalev-Shwartz. Download full books in PDF and EPUB format.

Understanding Machine Learning

Understanding Machine Learning PDF Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415

Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Understanding Machine Learning

Understanding Machine Learning PDF Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415

Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Boosting

Boosting PDF Author: Robert E. Schapire
Publisher: MIT Press
ISBN: 0262526034
Category : Computers
Languages : en
Pages : 544

Book Description
An accessible introduction and essential reference for an approach to machine learning that creates highly accurate prediction rules by combining many weak and inaccurate ones. Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate “rules of thumb.” A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.

Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Kamalika Chaudhuri
Publisher: Springer
ISBN: 9783319244853
Category : Computers
Languages : en
Pages : 0

Book Description
This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015. The 23 full papers presented in this volume were carefully reviewed and selected from 44 submissions. In addition the book contains 2 full papers summarizing the invited talks and 2 abstracts of invited talks. The papers are organized in topical sections named: inductive inference; learning from queries, teaching complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory.

Algorithmic Learning in a Random World

Algorithmic Learning in a Random World PDF Author: Vladimir Vovk
Publisher: Springer Science & Business Media
ISBN: 9780387001524
Category : Computers
Languages : en
Pages : 344

Book Description
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.

Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: José L. Balcázar
Publisher: Springer
ISBN: 3540466509
Category : Computers
Languages : en
Pages : 405

Book Description
This book constitutes the refereed proceedings of the 17th International Conference on Algorithmic Learning Theory, ALT 2006, held in Barcelona, Spain in October 2006, colocated with the 9th International Conference on Discovery Science, DS 2006. The 24 revised full papers presented together with the abstracts of five invited papers were carefully reviewed and selected from 53 submissions. The papers are dedicated to the theoretical foundations of machine learning.

Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Marcus Hutter
Publisher: Springer Science & Business Media
ISBN: 3540752242
Category : Computers
Languages : en
Pages : 415

Book Description
This book constitutes the refereed proceedings of the 18th International Conference on Algorithmic Learning Theory, ALT 2007, held in Sendai, Japan, October 1-4, 2007, co-located with the 10th International Conference on Discovery Science, DS 2007. The 25 revised full papers presented together with the abstracts of five invited papers were carefully reviewed and selected from 50 submissions. They are dedicated to the theoretical foundations of machine learning.

The Principles of Deep Learning Theory

The Principles of Deep Learning Theory PDF Author: Daniel A. Roberts
Publisher: Cambridge University Press
ISBN: 1316519333
Category : Computers
Languages : en
Pages : 473

Book Description
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

Algorithmic Learning

Algorithmic Learning PDF Author: Alan Hutchinson
Publisher: Oxford University Press, USA
ISBN:
Category : Computers
Languages : en
Pages : 472

Book Description
Machine learning is a rapidly changing field within artificial intelligence, as more algorithms are identified and a theory of which algorithm will suit which purpose emerges. Artificial Learning provides a comprehensive introduction to all aspects of the subject and will be both aninvaluable text for students and a reference for practitioners seeking an up-to-date review.

Scientific Discovery

Scientific Discovery PDF Author: Pat Langley
Publisher: MIT Press
ISBN: 9780262620529
Category : Computers
Languages : en
Pages : 374

Book Description
Scientific discovery is often regarded as romantic and creative--and hence unanalyzable--whereas the everyday process of verifying discoveries is sober and more suited to analysis. Yet this fascinating exploration of how scientific work proceeds argues that however sudden the moment of discovery may seem, the discovery process can be described and modeled. Using the methods and concepts of contemporary information-processing psychology (or cognitive science) the authors develop a series of artificial-intelligence programs that can simulate the human thought processes used to discover scientific laws. The programs--BACON, DALTON, GLAUBER, and STAHL--are all largely data-driven, that is, when presented with series of chemical or physical measurements they search for uniformities and linking elements, generating and checking hypotheses and creating new concepts as they go along. Scientific Discovery examines the nature of scientific research and reviews the arguments for and against a normative theory of discovery; describes the evolution of the BACON programs, which discover quantitative empirical laws and invent new concepts; presents programs that discover laws in qualitative and quantitative data; and ties the results together, suggesting how a combined and extended program might find research problems, invent new instruments, and invent appropriate problem representations. Numerous prominent historical examples of discoveries from physics and chemistry are used as tests for the programs and anchor the discussion concretely in the history of science.

Algorithmic Learning Theory

Algorithmic Learning Theory PDF Author: Osamu Watanabe
Publisher: Springer
ISBN: 3540467696
Category : Computers
Languages : en
Pages : 375

Book Description
This book constitutes the refereed proceedings of the 10th International Conference on Algorithmic Learning Theory, ALT'99, held in Tokyo, Japan, in December 1999. The 26 full papers presented were carefully reviewed and selected from a total of 51 submissions. Also included are three invited papers. The papers are organized in sections on Learning Dimension, Inductive Inference, Inductive Logic Programming, PAC Learning, Mathematical Tools for Learning, Learning Recursive Functions, Query Learning and On-Line Learning.