《模式识别》课程教学资源（书籍文献）Data Clustering - 50 Years Beyond K-means
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《模式识别》课程教学资源（书籍文献）Data Clustering - A Review（A.K. JAIN、M.N. MURTY、P.J. FLYNN）
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《模式识别》课程教学资源（书籍文献）A tutorial on Principal Components Analysis（Lindsay I Smith）
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《模式识别》课程教学资源（书籍文献）A Tutorial on Principal Component Analysis（Jonathon Shlens）
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《模式识别》课程教学资源（书籍文献）Sequential Minimal Optimization - A Fast Algorithm for Training Support Vector Machines（John C. Platt）
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《模式识别》课程教学资源（书籍文献）A Tutorial on Support Vector Machines for Pattern Recognition（CHRISTOPHER J.C. BURGES）
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《模式识别》课程教学资源（书籍文献）Introduction to Support Vector Learning
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《模式识别》课程教学资源（书籍文献）TRENDS & CONTROVERSIES TRENDS & CONTROVERSIES - Support vector machines
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《模式识别》课程教学资源（书籍文献）Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance
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In this paper, I provide a tutorial exposition on maximum likelihood estimation (MLE). The intended audience of this tutorial are researchers who practice mathematical modeling of cognition but are unfamiliar with the estimation method. Unlike least-squares estimation which is primarily a descriptive tool, MLE is a preferred method of parameter estimation in statistics and is an indispensable tool for many statistical modeling techniques, in particular in non-linear modeling with non-normal data. The purpose of this paper is to provide a good conceptual explanation of the method with illustrative examples so the reader can have a grasp of some of the basic principles
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