Description: Description: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP.
Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107.-Description: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP.
Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107. Platform: |
Size: 31744 |
Author:修宇 |
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Description: Support Vector Machines is a powerful methodology for solving problems in nonlinear classification and regression.
It is a matlab version.-Support Vector Machines is a powerful methamphetamine odology for solving problems in nonlinear CLAS sification and regression. It is a Matlab versi on. Platform: |
Size: 29696 |
Author:Mountain |
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Description: 基于模糊的最小支持响亮机,用来分类效果比一些常用的要好很多.-based on fuzzy loud support of the smallest machines, used classification results than some frequently used a lot nicer. Platform: |
Size: 1024 |
Author:fujiewei |
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Description: 通常情况下是线性的分类,但在复杂情况下,要把他转换到另一空间来,以便线性的分类,
SVM有其优势,还能做非线性的波束形成.-under normal circumstances is linear classification, but in complex situations, it is necessary to change his room to another, to the linear classification, SVM has its advantages, the ability to do nonlinear beam forming. Platform: |
Size: 3072 |
Author:fujiewei |
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Description: 支持向量机非线性回归通用matlab程序
支持向量机和BP神经网络非线性回归之比较研究-SVM nonlinear regression procedures common Matlab support vector machines and artificial neural networks to nonlinear In the Comparative Study Platform: |
Size: 4096 |
Author:sunwanghu |
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Description: Support Vector Machine is small sample method based on statistic learning theory. It is a new method to deal with the highly nonlinear classification and regression problems .It can better deal with the small sample, nonlinear and -Support Vector Machine is small sample method based on statistic learning theory. It is a new method to deal with the highly nonlinear classification and regression problems. It can better deal with the small sample, nonlinear and Platform: |
Size: 32768 |
Author:张真 |
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Description: 支持向量机的matlab实现，包括线性和非线性算法，以及介绍使用的说明文件。-Support vector machine matlab realize, including linear and nonlinear algorithms, and introduce the use of documentation. Platform: |
Size: 211968 |
Author:邵桂芳 |
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Description: 关于支持向量机的Matlab源代码，用于模式识别和非线性问题的程序。对于初学和不想具体了解支持向量机的很有用。-On Support Vector Machines Matlab source code for pattern recognition problems and nonlinear procedures. For beginner and do not want a specific understanding of support vector machine useful. Platform: |
Size: 124928 |
Author:kaku |
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Description: this a matlab coding of svm classification function.
when inputing training samples, training labels, testing samples, testing labels, and two parameters, the classification result is obtained.
linear svm and nonlinear svm can be selected.-this is a matlab coding of svm classification function. when inputing training samples, training labels, testing samples, testing labels, and two parameters, the classification result is obtained. linear svm and nonlinear svm can be selected. Platform: |
Size: 1024 |
Author:Phoenix |
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Description: 核主成分分析方法，是主成分分析的一种改进算法，是一种非线性的特征提取方法。
-Kernel principal component analysis, is the principal component analysis of an improved algorithm, is a nonlinear feature extraction method. Platform: |
Size: 1024 |
Author:叶子 |
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Description: SVM方法的基本思想是：定义最优线性超平面，并把寻找最优线性超平面的算法归结为求解一个凸规划问题。进而基于Mercer核展开定理，通过非线性映射φ，把样本空间映射到一个高维乃至于无穷维的特征空间（Hilbert空间），使在特征空间中可以应用线性学习机的方法解决样本空间中的高度非线性分类和回归等问题。svm 程序，即支持向量机的代码。-The basic idea of SVM method are: the definition of the optimal linear hyperplane, and the search algorithm for optimal linear hyperplane by solving a convex programming problem. Then based on Mercer nuclear expansion theorem, through a nonlinear mapping φ, the sample space is mapped to a high-dimensional and even infinite dimensional feature space (Hilbert space), so that in the feature space can be applied to solve the linear learning machine method, the sample space The highly nonlinear classification and regression problems. svm procedures that support vector machine code. Platform: |
Size: 117760 |
Author:秀 |
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Description: 支持向量机的研究现已成为机器学习领域中的研究热点，其理论基础是Vapnik[3]等提出的统计学习理论。统计学习理论采用结构风险最小化准则，在最小化样本点误差的同时，缩小模型泛化误差的上界，即最小化模型的结构风险，从而提高了模型的泛化能力，这一优点在小样本学习中更为突出。SVM理论正是在这一基础上发展而来的，经过十几年的研究和发展，已开始逐步应用于一些领域。在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，已经在模式识别、函数逼近和概率密度估计等方面取得了良好的效果。- Support Vector Machine (SVM) is a new machine learning technique in recent years developed based on statistical learning theory (SLT). It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. The theory and algorithm of SVC is studied at first, then, simulation is to recognize handwritten numeral with the Lib-SVM toolbox. At last, we study the result, which shows that the SVC can do the classification problem with good performance, shorter operation time and is more suitable for real-time implementation. Platform: |
Size: 1155072 |
Author:任修齐 |
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Description: svm线性和非线性的处理源代码。加了很多说明，希望对你有帮助-SVM linear and nonlinear processing the source code. Add a lot of shows, the hope is helpful to you Platform: |
Size: 3072 |
Author:xuezhuan |
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Description: matlab中的svm工具包，是标准化的svm算法，只能用于线性问题，非线性的需用libsvm或lssvm来解决。-svm in matlab Kit, standardized svm algorithm can only be used for linear problems, nonlinear the required libsvm or lssvm of resolve. Platform: |
Size: 125952 |
Author:王欣 |
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Description: 这是非线性svm分类器的matlab代码，不仅有代码，而且有数据。-This is matlab code of a nonlinear SVM classifier , which not only has the code, but the data.
Platform: |
Size: 3072 |
Author:wangzengqin |
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Description: SVM(Support Vector Machine)指的是支持向量机，是常见的一种判别方法。在机器学习领域，是一个有监督的学习模型，通常用来进行模式识别、分类以及回归分析。
Vapnik等人在多年研究统计学习理论基础上对线性分类器提出了另一种设计最佳准则。其原理也从线性可分说起，然后扩展到线性不可分的情况。甚至扩展到使用非线性函数中去，这种分类器被称为支持向量机(Support Vector Machine，简称SVM)。支持向量机的提出有很深的理论背景。
支持向量机方法是在后来提出的一种新方法。
SVM的主要思想可以概括为两点:
它是针对线性可分情况进行分析，对于线性不可分的情况，通过使用非线性映射算法将低维输入空间线性不可分的样本转化为高维特征空间使其线性可分，从而使得高维特征空间采用线性算法对样本的非线性特征进行线性分析成为可能。
它基于结构风险最小化理论之上在特征空间中构建最优超平面，使得学习器得到全局最优化，并且在整个样本空间的期望以某个概率满足一定上界。(442/5000
SVM(Support Vector Machine) refers to Support Vector Machine, which is a common discriminant method. In the field of machine learning, it is a supervised learning model, which is usually used for pattern recognition, classification and regression analysis.
Vapnik et al. proposed another design criterion for linear classifier on the basis of years of statistical learning theory. The principle is also derived from linear separability, and then extends to linear inseparability. Even extending to the use of nonlinear functions, this classifier is called Support Vector Machine (SVM). The support vector machine has a deep theoretical background.) Platform: |
Size: 633856 |
Author:飛上云端 |
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