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Nicolo Cesa-Bianchi 作者

Cambridge University Press

译者

2006-3-13 出版日期

408 页数

GBP 57.00 价格

Hardcover

丛书系列

9780521841085 图书编码

Prediction, Learning, and Games 在线电子书图书标签: 机器学习 OnlineLearning MachineLearning Online_Learning Learning_Theory GameTheory' 搜索引擎 ML

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发表于2024-12-23

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Prediction, Learning, and Games 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Prediction, Learning, and Games 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Online Learning研究必备。数学多，啃下来不易。

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人工智能机器学习中online learning算法的必读书籍，该算法非常有趣，已运用到诸如股票预测等领域并取得好成绩，其一个接一个的数据进入使得其与别的算法大不相同，书中对于数学的要求颇高，我好不容易读完，深感其精妙，希望在以后的研究工作中能使用到这个算法。

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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.

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