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libsvm-2.82.tar
- 我一直覺得 SVM 是個很有趣的東西，不過也一直沒辦法 (mostly 衝堂) 去聽林智仁老師 的 Data mining 跟 SVM 的課； 後來看了一些網路上的文件跟聽 kcwu 講了一下 libsvm 的用法後，就想整理一下，算是對於並不需要知道完整 SVM 理論的人提供使用 libsvm 的入門.-SVM I always think that is something very interesting, but we also have no way (mostly Chong Ton
svmmatlabSORCE
- 支撑矢量机 class CvSVM : public CvStatModel //继承自基类CvStatModel { public: // SVM type enum { C_SVC=100, NU_SVC=101, ONE_CLASS=102, EPS_SVR=103, NU_SVR=104 } //SVC是SVM分类器，SVR是SVM回归 // SVM kernel type -class Support Vector Machine CvSVM
svmmatlab4
- ％ 支持向量机Matlab工具箱1.0 - One-Class SVM, 一类支持向量机 ％ 使用平台 - Matlab6.5 希望对大家有用
libsvm-2.89
- LIBSVM 是台湾大学林智仁(Chih-Jen Lin)博士等开发设计的一个操作简单、 易于使用、快速有效的通用SVM 软件包，可以解决分类问题（包括C- SVC、 n - SVC ）、回归问题（包括e - SVR、n - SVR ）以及分布估计（one-class-SVM ） 等问题，提供了线性、多项式、径向基和S形函数四种常用的核函数供选择，可 以有效地解决多类问题、交叉验证选择参数、对不平衡样本加权、多类问题的概 率估计等.
svm_perf.tar.gz
- SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping h: X --> Y using labeled training examples (x1,y1), ..., (xn,yn). Unlike regula
gp425win32
- 易于使用、快速有效的通用SVM 软件包，可以解决分类问题（包括C- SVC、 n - SVC ）、回归问题（包括e - SVR、n - SVR ）以及分布估计（one-class-SVM -Easy to use, fast and effective generic SVM software package can solve the classification problems (including the C-SVC, n- SVC), regression (inclu
SVM_luzhenbo
- 支持向量机工具箱，包括了二种分类,二种回归,以及一种一类支持向量机算法。 -Support Vector Machine Toolbox, including the two kinds of classification, two kinds of regression, as well as a one-class SVM algorithm.
svm_perf
- SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping h: X --> Y using labeled training examples (x1,y1), ..., (xn,yn). Unlike reg
VSVMDecisionM
- VM分类器通常具有较高的分类精度。我这里不想过多的去说SVM是怎么回事，，只是提供一种使用SVM进行判别的方法。决策树与SVM的结合，可以分多类。 -VM classifier usually has high classification accuracy. I do not want too much to say that SVM is how, just a SVM is used to discriminate. Combination of decision tree and S
Multi-class-SVM--LS_SVMlab
- 工具箱：LS_SVMlab Classification_LS_SVMlab.m - 多类分类 Regression_LS_SVMlab.m - 函数拟合-Toolbox: LS_SVMlab Classification_LS_SVMlab.m- multi-class classification Regression_LS_SVMlab.m- function fitting
Multi-class-SVM--OSU_SVM3.00
- 一款基于svm的程序，在windows下运行，主要用于svm分类-Toolbox: OSU_SVM3.00 Classification_OSU_SVM.m- multi-class classification
logisitic_exercise
- 一种用于数据分类的算法，与svm的作用类似-In statistics, logistic regression or logit regression is a type of probabilistic classification model[1] used for predicting the outcome of a categorical dependent variable (i.e., a class label) based on one or more predictor v
svm
- support vector machine code for classification of multi class problem
libsvm_format
- Transform data to the format of an SVM package(libsvm) Each row represents one record [label] [index1]:[value1] [index2]:[value2] ... 2‐class label: +1, ‐1 n‐class label: 1..n
main
- This source code is for one time training of color images for the svm classification of the fruits (specifically used for apples). The SVM classifier used has been designed to work with multiple classes, whereas the code is utilizing only two class c
one-class-SVM
- 经典的单类相关向量机程序，比较实用，适合初学者-classical one-class SVM program，very practical and satisfied for the beginner
hiu_gp43
- 雅克比迭代求解线性方程组课设,包括最小二乘法、SVM、神经网络、1_k近邻法,基于多相结构的信道化接收机。- Jacobi iteration for solving linear equations class-based, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Channelized receiver based on multi-phase structure.
KUIxbsenceelement
- n类PSVm 算法程序,相对于svm运算速度较快,不错的 很好(N class PSVm algorithm program, compared with SVM fast calculation speed, good is very good)
FLSSVM
- 对目标函数的松弛变量引入一个参数μ，优化基本的最小二乘一类支持向量机算法。同样是解决线性问题，避免了二次规划的复杂问题(A parameter min is introduced into the relaxation variable of the objective function, and the basic least squares support vector machines algorithm is optimized. It also solves the linear pr
sa_svdd-master
- Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM regression, and nu-SVM regression. It also provides an automatic model