统计学习方法第二版PDF下载
统计学习方法，即机器学习方法，是计算机及其应用领域的一个重要课题。
本书分为两部分：监督学习和非监督学习。
系统介绍了统计学习的主要方法。它包括感知器、k近邻、朴素贝叶斯方法、决策树、逻辑回归和最大熵模型、支持向量机、提升方法、EM算法、隐马尔可夫模型和条件随机场，以及聚类、奇异值分解、主成分分析、潜在语义分析、概率潜在语义分析、，马尔可夫链蒙特卡罗方法、潜在Dirichlet分配和PageRank算法。除了介绍和总结统计学习、监督学习和非监督学习的四章外，每章还介绍了一种方法。本文试图从一个具体问题或一个实例入手，从表面到深层阐明观点，并进行必要的数学推导，使读者能够掌握统计学习方法的本质并学会使用。为了满足读者进一步学习的需要，本书还介绍了一些相关研究，给出了一些练习，并列出了主要参考文献。
本书是统计机器学习及相关课程的教学参考书。适用于文本数据挖掘、信息检索和自然语言处理专业的本科生和研究生。对从事计算机应用相关专业的研发人员也有一定的参考价值。
Statistical learning method, namely machine learning method, is an important subject in the field of computer and its application. This book is divided into two parts: supervised learning and unsupervised learning. It comprehensively and systematically introduces the main methods of statistical learning. It includes perceptron, k-nearest neighbor method, naive Bayes method, decision tree, logistic regression and maximum entropy model, support vector machine, lifting method, EM algorithm, hidden Markov model and conditional random field, as well as clustering method, singular value decomposition, principal component analysis, latent semantic analysis, probabilistic latent semantic analysis, Markov chain Monte Carlo method, latent Dirichlet assignment and PageRank algorithm. In addition to the four chapters on introduction and summary of statistical learning, supervised learning and unsupervised learning, each chapter introduces a method. The narration tries to start with specific problems or examples, clarify ideas from simple to deep, and give necessary mathematical derivation, so that readers can master the essence of statistical learning methods and learn to use them. In order to meet the needs of readers for further study, the book also introduces some relevant research, gives a small number of exercises, and lists the main references. This book is a teaching reference book for statistical machine learning and related courses. It is suitable for college students and postgraduates majoring in text data mining, information retrieval and natural language processing in Colleges and universities. It can also be used as a reference for R & D personnel engaged in computer application related majors.
统计学习方法（第2版）PDF
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