| 开本：16开 |
| 纸张：胶版纸 |
| 包装：平装-胶订 |
| 是否套装：否 |
| 国际标准书号ISBN：9787121500190 |
| 所属分类：图书>工业技术>能源与动力工程 |
内容简介
本书是机器学习的入门书，深入浅出地讲解机器学习的基础理论与应用，不仅注重给理论添加浅显易懂的解释和详述，而且探讨何种创新思维或科学思维可以产生或引导出某个理论，让学习者在学习理论过程中自然地培养创新思维与科学思维。本书知识点包括回归分析、近邻算法、决策树、贝叶斯分类器、支持向量机、模型性能评估、集成学习、降维方法、聚类、 算法与高斯混合模型、神经网络与深度学习等。本书每章都设计手工计算的应用例题，以演示理论解题和计算过程，帮助学习者理解和掌握理论。每章配有编程实践的实例，不仅示范解题的 代码，还示范解题思路、步骤和结果分析，培养学习者解决实际问题的能力。每章自然地融入科技强国、弘扬中华智慧与文化等内容。此外，每章的习题可巩固知识，对应的在线课程（中国大学，课程名：机器学习）可引领学习。 本书的配套教学资源有教学大纲、教学课件、源代码和案例素材等，读者可登录华信教育资源网免费下载；编程实例所用数据集在书中标注了下载途径。 本书可作为人工智能、计算机相关专业的教材，或供机器学习理论与应用的学习者使用参考。
作者简介
王开军，副教授，硕士生导师，现为福建师范大学计算机与网络空间安全学院教师，计算智能教研室主任，福建省人工智能学会理事。年在西安电子科技大学计算机应用专业获得博士学位。年在英国阿尔斯特大学计算机系作访问学者。
目 录
第章 绪论···································································································
机器学习简介·······················································································
机器学习方法的分类··············································································
机器学习框架·······················································································
的机器学习开发环境····································································
习题································································································
第章 回归分析··························································································
一元线性回归····················································································
多元线性回归····················································································
多项式回归·······················································································
回归分析的效果评价···········································································
逻辑回归··························································································
实例与编程求解·················································································
习题································································································第章 绪论···································································································
机器学习简介·······················································································
机器学习方法的分类··············································································
机器学习框架·······················································································
的机器学习开发环境····································································
习题································································································
第章 回归分析··························································································
一元线性回归····················································································
多元线性回归····················································································
多项式回归·······················································································
回归分析的效果评价···········································································
逻辑回归··························································································
实例与编程求解·················································································
习题································································································
第章 近邻算法························································································
近邻分类算法·················································································
值的选取························································································
距离度量··························································································
近邻回归算法·················································································
近邻算法的性能和特点·····································································
实例与编程求解·················································································
习题································································································
第章 决策树·····························································································
决策树的原理····················································································
决策树的构造算法··············································································
信息熵·····························································································
算法··························································································
算法·························································································
算法·······················································································
决策树的剪枝····················································································
决策树的特点····················································································
实例与编程求解·················································································
习题······························································································
第章 贝叶斯分类器····················································································
概率相关知识····················································································
贝叶斯分类原理·················································································
朴素贝叶斯分类器··············································································
实例与编程求解·················································································
习题································································································
第章 支持向量机·······················································································
二分类问题·······················································································
支持向量机分类原理··
显示全部信息
本书是机器学习的入门书，深入浅出地讲解机器学习的基础理论与应用，不仅注重给理论添加浅显易懂的解释和详述，而且探讨何种创新思维或科学思维可以产生或引导出某个理论，让学习者在学习理论过程中自然地培养创新思维与科学思维。本书知识点包括回归分析、近邻算法、决策树、贝叶斯分类器、支持向量机、模型性能评估、集成学习、降维方法、聚类、 算法与高斯混合模型、神经网络与深度学习等。本书每章都设计手工计算的应用例题，以演示理论解题和计算过程，帮助学习者理解和掌握理论。每章配有编程实践的实例，不仅示范解题的 代码，还示范解题思路、步骤和结果分析，培养学习者解决实际问题的能力。每章自然地融入科技强国、弘扬中华智慧与文化等内容。此外，每章的习题可巩固知识，对应的在线课程（中国大学，课程名：机器学习）可引领学习。 本书的配套教学资源有教学大纲、教学课件、源代码和案例素材等，读者可登录华信教育资源网免费下载；编程实例所用数据集在书中标注了下载途径。 本书可作为人工智能、计算机相关专业的教材，或供机器学习理论与应用的学习者使用参考。
作者简介
王开军，副教授，硕士生导师，现为福建师范大学计算机与网络空间安全学院教师，计算智能教研室主任，福建省人工智能学会理事。年在西安电子科技大学计算机应用专业获得博士学位。年在英国阿尔斯特大学计算机系作访问学者。
目 录
第章 绪论···································································································
机器学习简介·······················································································
机器学习方法的分类··············································································
机器学习框架·······················································································
的机器学习开发环境····································································
习题································································································
第章 回归分析··························································································
一元线性回归····················································································
多元线性回归····················································································
多项式回归·······················································································
回归分析的效果评价···········································································
逻辑回归··························································································
实例与编程求解·················································································
习题································································································第章 绪论···································································································
机器学习简介·······················································································
机器学习方法的分类··············································································
机器学习框架·······················································································
的机器学习开发环境····································································
习题································································································
第章 回归分析··························································································
一元线性回归····················································································
多元线性回归····················································································
多项式回归·······················································································
回归分析的效果评价···········································································
逻辑回归··························································································
实例与编程求解·················································································
习题································································································
第章 近邻算法························································································
近邻分类算法·················································································
值的选取························································································
距离度量··························································································
近邻回归算法·················································································
近邻算法的性能和特点·····································································
实例与编程求解·················································································
习题································································································
第章 决策树·····························································································
决策树的原理····················································································
决策树的构造算法··············································································
信息熵·····························································································
算法··························································································
算法·························································································
算法·······················································································
决策树的剪枝····················································································
决策树的特点····················································································
实例与编程求解·················································································
习题······························································································
第章 贝叶斯分类器····················································································
概率相关知识····················································································
贝叶斯分类原理·················································································
朴素贝叶斯分类器··············································································
实例与编程求解·················································································
习题································································································
第章 支持向量机·······················································································
二分类问题·······················································································
支持向量机分类原理··
显示全部信息