免费下载书籍地址：PDF下载地址
精美图片
Prediction, Learning, and Games预报，学习与游戏程序书籍详细信息
- ISBN：9780521841085
- 作者：暂无作者
- 出版社：暂无出版社
- 出版时间：2006-03
- 页数：104
- 价格：572.00
- 纸张：胶版纸
- 装帧：精装
- 开本：16开
- 语言：未知
- 丛书：暂无丛书
- TAG：暂无
- 豆瓣评分：暂无豆瓣评分
内容简介：
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.
书籍目录：
Preface
1 Introduction
1.1 Prediction
1.2 Learning
1.3 Games
1.4 A Gentle Start
1.5 A Note to the Reader
2 Prediction with Expert Advice
2.1 Weighted Average Prediction
2.2 An Optimal Bound
2.3 Bounds That Hold Uniformly over Time
2.4 An Improvement for Small Losses
2.5 Forecasters Using the Gradient of the Loss
2.6 Scaled Losses and Signed Games
2.7 The Multilinear Forecaster
2.8 The Exponential Forecaster for Signed Games
2.9 Simulatable Experts
2.10 Minimax Regret
2.11 Discounted Regret
2.12 Bibliographic Remarks
2.13 Exercises
3 Tight Bounds for Specific Losses
3.1 Introduction
3.2 Follow the Best Expert
3.3 Exp-concave Loss Functions
3.4 The Greedy Forecaster
3.5 The Aggregating Forecaster
3.6 Mixability for Certain Losses
3.7 General Lower Bounds
3.8 Bibliographic Remarks
3.9 Exercises
4 Randomized Prediction
4.1 Introduction
4.2 Weighted Average Forecasters
4.3 Follow the Perturbed Leader
4.4 Internal Regret
4.5 Calibration
4.6 Generalized Regret
4.7 Calibration with Checking Rules
4.8 Bibliographic Remarks
4.9 Exercises
5 Efficient Forecasters for Large Classes of Experts
5.1 Introduction
5.2 Tracking the Best Expert
5.3 Tree Experts
5.4 The Shortest Path Problem
5.5 Tracking the Best of Many Actions
5.6 Bibliographic Remarks
5.7 Exercises
6 Prediction with Limited Feedback
6.1 Introduction
6.2 Label Efficient Prediction
6.3 Lower Bounds
6.4 Partial Monitoring
6.5 A General Forecaster for Partial Monitoring
6.6 Hannah Consistency and Partial Monitoring
6.7 Multi-armed Bandit Problems
6.8 An Improved Bandit Strategy
6.9 Lower Bounds for the Bandit Problem
6.10 How to Select the Best Action
6.11 Bibliographic Remarks
6.12 Exercises
7 Prediction and Playing Games
7.1 Games and Equilibria
7.2 Minimax Theorems
7.3 Repeated Two-Player Zero-Sum Games
7.4 Correlated Equilibrium and Internal Regret
7.5 Unknown Games: Game-Theoretic Bandits
7.6 Calibration and Correlated Equilibrium
7.7 Blackwell's Approachability Theorem
7.8 Potential-based Approachability
7.9 Convergence to Nash Equilibria
7.10 Convergence in Unknown Games
7.11 Playing Against Opponents That React
7.12 Bibliographic Remarks
7.13 Exercises
8 Absolute loss
9 Logarithmic loss
10 Sequential investment
11 Linear pattern recognition
12 Linear classification
Appendix
References
Author Index
Subject Index
作者介绍：
暂无相关内容，正在全力查找中
出版社信息：
暂无出版社相关信息，正在全力查找中！
书籍摘录：
暂无相关书籍摘录，正在全力查找中！
在线阅读/听书/购买/PDF下载地址：
在线阅读地址：Prediction, Learning, and Games预报，学习与游戏程序在线阅读
在线听书地址：Prediction, Learning, and Games预报，学习与游戏程序在线收听
在线购买地址：Prediction, Learning, and Games预报，学习与游戏程序在线购买
原文赏析：
暂无原文赏析，正在全力查找中！
其它内容：
编辑推荐
作者简介：
Nicolò Cesa-Bianchi is Professor of Computer Science at the University of Milan, Italy. His research interests include learning theory, pattern analysis, and worst-case analysis of algorithms. He is action editor of The Machine Learning Journal. Gábor Lugosi has been working on various problems in pattern classification, nonparametric statistics, statistical learning theory, game theory, probability, and information theory. He is co-author of the monographs, A Probabilistic Theory of Pattern Recognition and Combinatorial Methods of Density Estimation. He has been an associate editor of various journals including The IEEE Transactions of Information Theory, Test, ESAIM: Probability and Statistics and Statistics and Decisions.
书籍真实打分
故事情节：6分
人物塑造：5分
主题深度：9分
文字风格：4分
语言运用：6分
文笔流畅：6分
思想传递：3分
知识深度：7分
知识广度：8分
实用性：7分
章节划分：4分
结构布局：3分
新颖与独特：5分
情感共鸣：8分
引人入胜：9分
现实相关：9分
沉浸感：8分
事实准确性：3分
文化贡献：6分
网站评分
书籍多样性：4分
书籍信息完全性：8分
网站更新速度：3分
使用便利性：9分
书籍清晰度：3分
书籍格式兼容性：4分
是否包含广告：3分
加载速度：9分
安全性：4分
稳定性：3分
搜索功能：9分
下载便捷性：4分
下载点评
- 差评(234+)
- 实惠(349+)
- 书籍多(312+)
- epub(398+)
- 经典(467+)
- 方便(676+)
- 体验满分(259+)
- 中评多(166+)
- azw3(263+)
- 三星好评(106+)
- 目录完整(574+)
- 速度慢(315+)
下载评价
网友 谢***灵：推荐，啥格式都有
网友 温***欣：可以可以可以
网友 濮***彤：好棒啊！图书很全
网友 车***波：很好，下载出来的内容没有乱码。
网友 堵***格：OK，还可以
网友 林***艳：很好，能找到很多平常找不到的书。
网友 后***之：强烈推荐！无论下载速度还是书籍内容都没话说 真的很良心！
网友 国***舒：中评，付点钱这里能找到就找到了，找不到别的地方也不一定能找到
网友 薛***玉：就是我想要的！！！
网友 冉***兮：如果满分一百分，我愿意给你99分，剩下一分怕你骄傲
网友 宫***玉：我说完了。
网友 权***波：收费就是好，还可以多种搜索，实在不行直接留言，24小时没发到你邮箱自动退款的！
网友 寇***音：好，真的挺使用的！
网友 寿***芳：可以在线转化哦
网友 潘***丽：这里能在线转化，直接选择一款就可以了，用他这个转很方便的
网友 丁***菱：好好好好好好好好好好好好好好好好好好好好好好好好好