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Jingdong book

图解机器学习

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Jingdong book

图解机器学习

Name: 图解机器学习
Brand: Jingdong book
SKU: 3118033531
Price: 31.68 USD
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187张图解轻松入门
提供可执行的Matlab程序代码
覆盖机器学习中最经典、用途最广的算法
专业实用
东京大学教授、机器学习专业专家执笔，浓缩机器学习的关键知识点
图文并茂
187张图示帮助理解，详略得当，为读懂大部头开路。
角度新颖
基于最小二乘法讲解各种有监督学习的回归和分类算法，以及无监督学习算法。
实战导向
配有可执行的MATLAB程序代码，边学习边实践。

Content Description

《图解机器学习》用丰富的图示，从ZUI小二乘法出发，对基于ZUI小二乘法实现的各种机器学习算法进行了详细的介绍。第Ⅰ部分介绍了机器学习领域的概况；第Ⅱ部分和第Ⅲ部分分别介绍了各种有监督的回归算法和分类算法；第Ⅳ部分介绍了各种监督学习算法；第Ⅴ部分介绍了机器学习领域中的新兴算法。书中大部分算法都有相应的MATLAB程序源代码，可以用来进行简单的测试。

Author Description

杉山将，1974年生于大阪。东京工业大学计算机工程学博士毕业，现为东京大学教授、日本国立信息学研究所客座教授。主要从事机器学习的理论研究和算法开发，以及在信号和图像处理等方面的应用。2011年获日本信息处理学会长尾真纪念特别奖。著有《统计机器学习》、DensityRatioEstimationinMachineLearning等。同时也是PatternRecognitionandMachineLearning日文版的译者之一。

许永伟，2009年赴东京大学攻读博士学位，现于东京大学空间信息科学研究所从事博士后研究（特任研究员）。主要研究方向为模式识别与机器学习、图像处理与计算机视觉，对数据挖掘、大数据和信息架构有浓厚兴趣。

Catalogue

第I部分绪论
第1章什么是机器学习
1．1学习的种类
1．2机器学习任务的例子
1．3机器学习的方法
第2章学习模型
2．1线性模型
2．2核模型
2．3层级模型
第II部分有监督回归
第3章最小二乘学习法
3．1最小二乘学习法
3．2最小二乘解的性质
3．3大规模数据的学习算法
第4章带有约束条件的最小二乘法
4．1部分空间约束的最小二乘学习法
4．2ｌ2 约束的最小二乘学习法
4．3模型选择
第5章稀疏学习
5．1ｌ1 约束的最小二乘学习法
5．2ｌ1 约束的最小二乘学习的求解方法
5．3通过稀疏学习进行特征选择
5．4ｌp约束的最小二乘学习法
5．5ｌ1+ｌ2 约束的最小二乘学习法
第6章鲁棒学习
6．1ｌ1 损失最小化学习
6．2Huber损失最小化学习
6．3图基损失最小化学习
6．4ｌ1 约束的Huber损失最小化学习
第III部分有监督分类
第7章基于最小二乘法的分类
7．1最小二乘分类
7．20/1 损失和间隔
7．3多类别的情形
第8章支持向量机分类
8．1间隔最大化分类
8．2支持向量机分类器的求解方法
8．3稀疏性
8．4使用核映射的非线性模型
8．5使用Hinge损失最小化学习来解释
8．6使用Ramp损失的鲁棒学习
第9章集成分类
9．1剪枝分类
9．2Bagging学习法
9．3Boosting 学习法
第10章概率分类法
10．1Logistic回归
10．2最小二乘概率分类
第11 章序列数据的分类
11．1序列数据的模型化
11．2条件随机场模型的学习
11．3利用条件随机场模型对标签序列进行预测
第IV部分监督学习
第12章异常检测
12．1局部异常因子
12．2支持向量机异常检测
12．3基于密度比的异常检测
第13章监督降维
13．1线性降维的原理
13．2主成分分析
13．3局部保持投影
13．4核函数主成分分析
13．5拉普拉斯特征映射
第14章聚类
14．1K均值聚类
14．2核K均值聚类
14．3谱聚类
14．4调整参数的自动选取
第V部分新兴机器学习算法
第15章在线学习
15．1被动攻击学习
15．2适应正则化学习
第16章半监督学习
16．1灵活应用输入数据的流形构造
16．2拉普拉斯正则化最小二乘学习的求解方法
16．3拉普拉斯正则化的解释
第17章监督降维
17．1与分类问题相对应的判别分析
17．2充分降维
第18章迁移学习
18．1协变量移位下的迁移学习
18．2类别平衡变化下的迁移学习
第19章多任务学习
19．1使用最小二乘回归的多任务学习
19．2使用最小二乘概率分类器的多任务学习
19．3多次维输出函数的学习
第VI部分结语
第20章总结与展望
参考文献

Introduction

机器学习领域是深不可测的吗？人工智能是天方夜谭吗？时至今日，机器学习研究的重要性与可行性已得到广泛承认，并在模式识别、通信、控制、金融、机器人、生物信息学等许多领域都有着广泛的应用。
如何自动归类筛选邮件和网页？如何向大家推荐你可能感兴趣的人？如何预测整体市场行情的好坏？如何从统计学的角度对照片进行归类？本书就介绍了这样一些算法。
如果想得到最通俗、简洁的讲解，本书最为合适。
如果想立即知道算法的性能，并期望有可运行的源代码，本书最为方便。
很多人都是看着日本的动画长大的。殊不知，大部分日本人都具有熟练的绘画能力。他们总可以把复杂、枯燥的事物用惟妙惟肖的漫画生动地表达出来。广告、网页、海报，甚至政府公告都图文并茂。市面上也有不计其数的“图解……”“图说……”一类的书籍。本书就是其中一例，这也是本书的最大特点。
杉山将博士今年赴任东京大学教授，他在机器学习领域颇有建树。他的研究室吸引了来自世界各地的机器学习研究者。本书承袭了日本特有的绘画特色，依靠作者丰富的机器学习经验，用最精简的文字，将原本复杂抽象的数学原理，用形象的漫画与数据图形进行了清晰的说明。作者也将最前沿和最核心的研究成果汇集到了本书之中。
本书的侧重点不在于机器学习原理的相关推导，而在于结论的分析和应用。读者朋友可以更快地掌握各种算法的特点和使用方法，提纲挈领地消化应用，而不必拘泥于算法的细节不能自拔。另外，本书旁征博引，图文并茂，结构清晰，范例实用丰富，深入浅出地说明了机器学习中最典型和用途最广泛的算法。
本书内容覆盖面广，不但与市面上众多的机器学习书籍并无重复，更可与其互为补充。大部分算法都有简洁、现成的MATLAB源代码，读者朋友可以轻松地进行验证。以此为原型，再稍加修改扩充，即可做出为自己所用的项目代码。
机器学习领域日新月异，书中所涉及的概念和术语数目繁多，且有许多概念和术语目前尚无公认的中文译法。如果有不合读者朋友习惯的术语出现，请参考译者注，确认其原始词意。
本译稿得到了图灵公司编辑的悉心指导，她们为保证本书的质量做了大量的补译、校正及编辑工作，在此表示深深的谢意。
许永伟
2014年12月于东京

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