Prediction Learning and Games

This important new text and reference for researchers and students in machine learning, game theory, statistics and information theory offers the first comprehensive treatment of the problem of predicting individual sequences.

Prediction  Learning  and Games

This important new text and reference for researchers and students in machine learning, game theory, statistics and information theory offers the first comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections. Old and new forecasting methods are described in a mathematically precise way in order to characterize their theoretical limitations and possibilities.

More Books:

Prediction, Learning, and Games
Language: en
Pages: 406
Authors: Nicolo Cesa-Bianchi, Gabor Lugosi
Categories: Computers
Type: BOOK - Published: 2006-03-13 - Publisher: Cambridge University Press

This important new text and reference for researchers and students in machine learning, game theory, statistics and information theory offers the first comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating
Prediction, Learning, and Games
Language: en
Pages:
Authors: Nicolo Cesa-Bianchi, Gabor Lugosi
Categories: Computers
Type: BOOK - Published: 2006-03-13 - Publisher: Cambridge University Press

This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet,
Prediction, Learning, and Games
Language: en
Pages: 394
Authors: Nicol├▓ Cesa-Bianchi
Categories: Computer algorithms
Type: BOOK - Published: 2006 - Publisher:

The central theme here is a model of prediction using expert advice, a general framework within which many related problems can be cast and discussed, including repeated game playing, adaptive data compression, sequential investment in the stock market, and sequential pattern analysis.
Algorithmic Game Theory
Language: en
Pages:
Authors: Noam Nisan, Tim Roughgarden, Eva Tardos, Vijay V. Vazirani
Categories: Computers
Type: BOOK - Published: 2007-09-24 - Publisher: Cambridge University Press

In recent years game theory has had a substantial impact on computer science, especially on Internet- and e-commerce-related issues. Algorithmic Game Theory, first published in 2007, develops the central ideas and results of this exciting area in a clear and succinct manner. More than 40 of the top researchers in
Machine Learning and AI in Finance
Language: en
Pages: 130
Authors: German Creamer, Gary Kazantsev, Tomaso Aste
Categories: Business & Economics
Type: BOOK - Published: 2021-04-05 - Publisher: Routledge

The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly