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Support Vector Machines for Pattern Classification.pdf

Since the introduction of support vector machines, we have witnessed the huge development in theory, models, and applications of what is so-called kernel-based methods: advancement in generalization theory, kernel classifiers and regressors and their variants, various feature selection and extraction methods, and wide variety of applications such as pattern classification and regressions in biology, medicine, chemistry, as well as computer science.
2011-11-15 上传大小:7.81MB
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评论 共5条

juguangliang 支持向量机 比较容易看懂
2015-01-30
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zhuyingxiao 介绍不错的一本关于支持向量机分类的文章
2014-08-26
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mark1615 适合初学者
2014-08-23
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A Tutorial On Support Vector Machines For Pattern Recognition

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Coursera Machine Learning 第七周week7ex6Support Vector Machines编程全套满分题目+注释选做

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Support Vector Machines for Pattern Classification

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A Tutorial on Support Vector Machines for Pattern Recognition

A Tutorial on Support Vector Machines for Pattern Recognition 经典svm介绍

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模式分类机器学习经典书籍大全

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研究svm入手级别的三篇paper

研究svm入手级别的三篇paper: 00_Sequential Minimal Optimization- A Fast Algorithm for Training Support Vector Machines 01_A Tutorial on Support Vector Machines for Pattern recognition(Burges1998) 02_SVM(Hearst-etal1998)

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support vector machines by Ingo Steinwart

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Deep Learning using Linear Support Vector Machines实现

Deep Learning using Linear Support Vector Machines的简单实现代码

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A tutorial on support vector machines for pattern recognition 原本和中文翻译

A tutorial on support vector machines for pattern recognition 的原文和对应的中文翻译 是SVM最为经典的资料之一

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Opencv2.4.9源码分析——Support Vector Machines

本文档共分为三个部分,第一个部分介绍SVM的原理,我们全面介绍了5中常用的SVM算法:C-SVC、ν-SVC、单类SVM、ε-SVR和ν-SVR,其中C-SVC和ν-SVC不仅介绍了处理两类分类问题的情况,还介绍处理多类问题的情况。在具体求解SVM过程中,我们介绍了SMO算法和广义SMO算法。第二个部分我们给出了OpenCV中SVM程序的详细注解。第三个部分我们给出了一个基于OpenCV的SVM算法的简单应用实例。

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ApplyingSupport VectorMachinestoImbalancedDatasets

Abstract. Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in gene profiling and detecting credit card fraud). This paper discusses the factors behind this failure and explains why the common strategy of undersampling the training data may not be the best choice for SVM. We then propose an algorithm for overcoming these problems which is based on a variant of the SMOTE algorithm by Chawla et al, combined with Veropoulos et al’s different error costs algorithm. We compare the performance of our algorithm against these two algorithms, along with undersampling and regular SVM and show that our algorithm outperforms all of them.

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Support Vector Machines

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Learning with Support Vector Machines

Learning with Support Vector Machines

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Sphere-Structured Support Vector Machines for Multi-class Pattern Recognition

超球结构支持向量机用于多分类问题。Support vector machines (SVM) are learning algorithms derived from statistical learning theory. The SVM approach was originally developed for binary classification problems. For solving multi-class classification problem, there are some methods such as one-against-rest, one-against-one, all- together and so on. But the computing time of all these methods are too long to solve large scale problem. In this paper SVMs architectures for multi-class problems are discussed, in particular we provide a new algorithm called sphere- structured SVMs to solve the multi-class problem. We show the algorithm in detail and analyze its characteristics. Not only the number of convex quadratic programming problems in sphere-structured SVMs is small, but also the number of variables in each programming is least. The computing time of classification is reduced. Otherwise, the characteristics of sphere-structured SVMs make expand data easily.

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A Tutorial on Support Vector Machines for Pattern

Abstract. The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.

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A Tutorial on Support Vector Machines for Pattern Recognition.pdf

关于机器学习中支持向量机的综述。非常适合初学者以及写作参考。

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Support Vector Machines

Support Vector Machines for Classification and Regression

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Support Vector Machines Explained

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Lagrangian Support Vector Machines

该书对支持向量机做了详细的描述,原版英文

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