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.
Online Learning研究必備。數學多,啃下來不易。
评分現在上課學習都得被強製看全英文書,我是真的纍
评分現在上課學習都得被強製看全英文書,我是真的纍
评分現在上課學習都得被強製看全英文書,我是真的纍
评分人工智能機器學習中online learning算法的必讀書籍,該算法非常有趣,已運用到諸如股票預測等領域並取得好成績,其一個接一個的數據進入使得其與彆的算法大不相同,書中對於數學的要求頗高,我好不容易讀完,深感其精妙,希望在以後的研究工作中能使用到這個算法。
本站所有內容均為互聯網搜索引擎提供的公開搜索信息,本站不存儲任何數據與內容,任何內容與數據均與本站無關,如有需要請聯繫相關搜索引擎包括但不限於百度,google,bing,sogou 等
© 2025 onlinetoolsland.com All Rights Reserved. 本本书屋 版权所有