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Jingdong book

机器学习

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Jingdong book

机器学习

Name: 机器学习
Brand: Jingdong book
SKU: 3118075291
Price: 20.20 USD
Availability: OutOfStock

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如何让计算机随着经验的积累自动提高性能？这就是机器学习的目的。《机器学习／计算机科学丛书》展示了机器学习中核心的算法和理论，并阐明了算法的运行过程。《机器学习／计算机科学丛书》综合了许多的研究成果，例如统计学、人工智能、哲学、信息论、生物学、认知科学、计算复杂性和控制论等，并以此来理解问题的背景、算法和其中的隐含假定。

Content Description

《机器学习／计算机科学丛书》展示了机器学习中核心的算法和理论，并阐明了算法的运行过程。《机器学习／计算机科学丛书》综合了许多的研究成果，例如统计学、人工智能、哲学、信息论、生物学、认知科学、计算复杂性和控制论等，并以此来理解问题的背景、算法和其中的隐含假定。《机器学习／计算机科学丛书》可作为计算机专业本科生、研究生教材，也可作为相关领域研究人员、教师的参考书。

Author Description

Tom M.Mitchell，是卡内基梅隆大学的教授，讲授“机器学习”等多门课程；美国人工智能协会（AAAL）的主席；美国《Machine Learning》杂志、国际机器学习年度会议（ICML）的创始人；多种技术杂志的撰稿人，曾发表过许多文章，出版过多本专著，是机器学习领域的知名学者。

Catalogue

出版者的话
专家指导委员会
译者序
前言
第1章引言
1.1学习问题的标准描述
1.2设计一个学习系统
1.2.1 选择训练经验
1.2.2 选择目标函数
1.2.3选择目标函数的表示
1.2.4选择函数逼近算法
1.2.5最终设计
1.3机器学习的一些观点和问题
1.4如何阅读本书
1.5小结和补充读物
习题

第2章概念学习和一般到特殊序
2.1简介
2.2概念学习任务
2.2.1术语定义
2.2.2归纳学习假设
2.3作为搜索的概念学习
2.4FIND-S：寻找极大特殊假设
2.5变型空间和候选消除算法
2.5.1表示
2.5.2 列表后消除算法
2.5.3变型空间的更简洁表示
2.5.4候选消除学习算法
2.5.5算法的举例
2.6关于变型空间和候选消除的说明
2.6.1候选消除算法是否会收敛到正确的假设
2.6.2 下一步需要什么样的训练样例
2.6.3怎样使用不完全学习概念
2.7归纳偏置
2.7.1一个有偏的假设空间
2.7.2无偏的学习器
2.7.3无偏学习的无用性
2.8小结和补充读物
习题

第3章决策树学习
3.1 简介
3.2决策树表示法
3.3决策树学习的适用问题
3.4基本的决策树学习算法
3.4.1哪个属性是最佳的分类属性
3.4.2举例
3.5决策树学习中的假设空间搜索
3.6决策树学习的归纳偏置
3.6.1限定偏置和优选偏置
3.6.2为什么短的假设优先
3.7决策树学习的常见问题
3.7.1避免过度拟合数据
3.7.2 合并连续值属性
3.7.3属性选择的其他度量标准
3.7.4处理缺少属性值的训练样例
3.7.5处理不同代价的属性
3.8小结和补充读物
习题

第4章人工神经网络
4.1简介
4.2神经网络表示
4.3适合神经网络学习的问题
4.4感知器
4.4.1感知器的表征能力
4.4.2感知器训练法则
4.4.3 梯度下降和delta法则
4.4.4小结
……
第5章评估假设
第6章贝叶斯学习
第7章计算学习理论
第8章基于实例的学习
第9章遗传算法
第10章学习规则集合
第11章分析这习
第12章归纳和分析学习的结合
第13章增强学习

Introduction

第1章引言

自从计算机问世以来，人们就想知道它们能不能自我学习。如果我们理解了计算机学习的内在机制，即怎样使它们根据经验来自动提高，那么影响将是空前的。想像一下，在未来，计算机能从医疗记录中学习，获取治疗新疾病最有效的方法；住宅管理系统分析住户的用电模式，以降低能源消耗；个人软件助理跟踪用户的兴趣，并为其选择最感兴趣的在线早间新闻。对计算机学习的成功理解将开辟出许多全新的应用领域，并使其计算能力和可定制性上升到新的层次。同时，透彻理解机器学习的信息处理算法，也会有助于更好地理解人类的学习能力（及缺陷）。

目前，我们还不知道怎样使计算机具备和人类一样强大的学习能力。然而，一些针对特定学习任务的算法已经产生。关于学习的理论认识已开始逐步形成。人们开发出很多实践性的计算机程序来实现不同类型的学习，一些商业化的应用也已经出现。例如，对于语音识别这样的课题，迄今为止，基于机器学习的算法明显胜过其他的方法。在数据挖掘领域，机器学习算法理所当然地得到应用，从包含设备维护记录、借贷申请、金融交易、医疗记录等信息的大型数据库中发现有价值的信息。随着对计算机认识的日益成熟，机器学习必将在计算机科学和技术中扮演越来越重要的角色！

我们可以通过一些专项成果看到机器学习这门技术的现状：计算机已经能够成功地识别人类的讲话（Waibel1989，Leel989）；预测肺炎患者的康复率（Cooperetal．1997）；检测信用卡的欺诈；在高速公路上自动驾驶汽车(Pomerleau1989)；以接近人类世界冠军的水平对弈西洋双陆棋（Tesauro1992，1995）。

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Jingdong book

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