How the Quest for the Ultimate Learning Machine Will Remake Our World
Author: Pedro Domingos
Pubpsher: Penguin UK
A spell-binding quest for the one algorithm capable of deriving all knowledge from data, including a cure for cancer Society is changing, one learning algorithm at a time, from search engines to online dating, personalized medicine to predicting the stock market. But learning algorithms are not just about Big Data - these algorithms take raw data and make it useful by creating more algorithms. This is something new under the sun: a technology that builds itself. In The Master Algorithm, Pedro Domingos reveals how machine learning is remaking business, politics, science and war. And he takes us on an awe-inspiring quest to find 'The Master Algorithm' - a universal learner capable of deriving all knowledge from data.
This volume grew out of a workshop designed to bring together researchers from different fields and includes contributions from workers in Bayesian analysis, machine learning, neural nets, PAC and VC theory, classical sampling theory statistics and the statistical physics of learning. The contributions present a bird's-eye view of the subject.
Release on 2006-09-29 | by Hans Ulrich Simon,Gábor Lugosi
19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings
Author: Hans Ulrich Simon,Gábor Lugosi
This book constitutes the refereed proceedings of the 19th Annual Conference on Learning Theory, COLT 2006, held in Pittsburgh, Pennsylvania, USA, June 2006. The book presents 43 revised full papers together with 2 articles on open problems and 3 invited lectures. The papers cover a wide range of topics including clustering, un- and semi-supervised learning, statistical learning theory, regularized learning and kernel methods, query learning and teaching, inductive inference, and more.
Release on 2001-07-04 | by David Helmbold,Bob Williamson
14th Annual Conference on Computational Learning Theory, COLT 2001 and 5th European Conference on Computational Learning Theory, EuroCOLT 2001, Amsterdam, The Netherlands, July 16-19, 2001, Proceedings
Author: David Helmbold,Bob Williamson
Pubpsher: Springer Science & Business Media
This book constitutes the refereed proceedings of the 14th Annual and 5th European Conferences on Computational Learning Theory, COLT/EuroCOLT 2001, held in Amsterdam, The Netherlands, in July 2001. The 40 revised full papers presented together with one invited paper were carefully reviewed and selected from a total of 69 submissions. All current aspects of computational learning and its applications in a variety of fields are addressed.
This book constitutes the refereed proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008, held in Budapest, Hungary, in October 2008, co-located with the 11th International Conference on Discovery Science, DS 2008. The 31 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 46 submissions. The papers are dedicated to the theoretical foundations of machine learning; they address topics such as statistical learning; probability and stochastic processes; boosting and experts; active and query learning; and inductive inference.
Release on 1989 | by Nick Littlestone,Manfred Warmuth
Author: Nick Littlestone,Manfred Warmuth
Abstract: "We study the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be made in each, and the goal of the learner is to make few mistakes. We are interested in the case that the learner has reason to believe that one of some pool of known algorithms will perform well, but the learner does not know which one. A simple and effective method, based on weighted voting, is introduced for constructing a compund algorithm in such a circumstance. We call this method the Weighted Majority Algorithm. We show that this algorithm is robust w.r.t. errors in the data. We discuss various versions of the Weighted Majority Algorithm and prove mistake bounds for them that are closely related to the mistake bounds of the best algorithms of the pool.
Release on 2007-10-24 | by S.N. Sivanandam,S. N. Deepa
Author: S.N. Sivanandam,S. N. Deepa
Pubpsher: Springer Science & Business Media
This book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. In addition, the book presents implementation of optimization problems using C and C++ as well as simulated solutions for genetic algorithm problems using MATLAB 7.0. It also includes application case studies on genetic algorithms in emerging fields.
MASCOTS 2004 looks at how-models can be calibrated and validated against real-world observations. The papers explore wireless and mobile networks, networks and protocols, stochastic models, queueing networks, Internet architecture and applications, P2P systems, routing algorithms, and storage systems.
Abstract: "We consider the problem of learning of an integer lattice of Z[superscript k] in an on-line fashion. That is, the learning algorithm is given a sequence of k-tuples of integers and predicts for each tuple in the sequence whether it lies in a hidden target lattice of Z[superscript k]. The goal of the algorithm is to minimize the number of prediction mistakes. We give an efficient learning algorithm with an absolute mistake bound of [formula], where n is the maximum component of any tuple seen.