An Elementary Introduction to Statistical Learning Theory 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


An Elementary Introduction to Statistical Learning Theory

简体网页||繁体网页
Sanjeev Kulkarni 作者
Wiley
译者
2011-8-2 出版日期
232 页数
USD 122.00 价格
Hardcover
丛书系列
9780470641835 图书编码

An Elementary Introduction to Statistical Learning Theory 在线电子书 图书标签: 机器学习  统计学习  统计哲学  数学  machine_learning  MachineLearning  统计学  归纳逻辑   


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An Elementary Introduction to Statistical Learning Theory 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

An Elementary Introduction to Statistical Learning Theory 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

An Elementary Introduction to Statistical Learning Theory 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



An Elementary Introduction to Statistical Learning Theory 在线电子书 用户评价

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虽然近一周来面试占去了相当多的时间,但是花了这么长时间才读完这本真是有点不可饶恕,此书很基本,但是对于那些只看算法与实现的朋友来说,依然是有料的,导论导论,窃以为勾勒出了大致思考的几个方向,并提出了问题,就已经很够了,并且自己还从中有了一些思考。 不过最大的问题在于,贝叶斯凭什么这么牛逼?

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我就想知道Harman写了哪些章节??

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我就想知道Harman写了哪些章节??

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虽然近一周来面试占去了相当多的时间,但是花了这么长时间才读完这本真是有点不可饶恕,此书很基本,但是对于那些只看算法与实现的朋友来说,依然是有料的,导论导论,窃以为勾勒出了大致思考的几个方向,并提出了问题,就已经很够了,并且自己还从中有了一些思考。 不过最大的问题在于,贝叶斯凭什么这么牛逼?

评分

虽然近一周来面试占去了相当多的时间,但是花了这么长时间才读完这本真是有点不可饶恕,此书很基本,但是对于那些只看算法与实现的朋友来说,依然是有料的,导论导论,窃以为勾勒出了大致思考的几个方向,并提出了问题,就已经很够了,并且自己还从中有了一些思考。 不过最大的问题在于,贝叶斯凭什么这么牛逼?

An Elementary Introduction to Statistical Learning Theory 在线电子书 著者简介


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An Elementary Introduction to Statistical Learning Theory 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

An Elementary Introduction to Statistical Learning Theory 在线电子书 图书描述

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning</bA joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inferencePromoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boostingAppendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further studyAn Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduatelevels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.

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