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svm
- 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 mo
deboor-cox.rar
- 目的:运用强化学习!多分类器集成!降维方法等最新计算机技术,结合细胞病理知识,设计制作/智能化肺癌细胞病理图像诊断系统0"方法:采集细胞图像,运用基于强化学习的图像分割法将细胞区域从背景中分离出来 运用基于样条和改进2方法对重叠细胞进行分离和重构 提取40个细胞特征用于贝叶斯!支持向量机!紧邻和决策树4种分类器,集成产生肺癌细胞分类结果 建立肺癌细胞病理图库,运用基于等降维方法对细胞进行比对,给予未定型癌细胞分类"结果:/智能化肺癌细胞病理诊断系统0应用于临床随机1200例肺
libsvm_setup
- 这是台湾大学林智仁(Lin Chih-Jen)副教授等人开发的SVM模式识别与回归的软件包，该软件可以解决C-SVM分类、-SVM分类、-SVM回归和-SVM回归等问题，包括基于一对一算法的多类模式识别问题。-This is the National Taiwan University, Lin Zhiren (Lin Chih-Jen), associate professor, who developed SVM pattern recognition and regression of t
libsvm-mat-2[1].89-3
- svm多分类器，包括多分类和GA算法和PSO算法优化的SVM-svm multi-classifier, including the multi-classification and GA algorithm and PSO algorithm for optimization of SVM
Main_SVM_One_Class
- svm Main_SVM_One_Class 用于svm分类的 在matlab中运用 -svm classification svm Main_SVM_One_Class for use in matlab
LS-SVMlab1.5
- 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-SVM software package can solve the classification problems (including the C-SVC, n- SVC), regression (including e- SVR, n- SVR) as well as the distribution of estim
libsvm-2.89
- 是一種線性方成的分類器。SVM透過統計的方式將雜亂的資料以NN的方式分成兩類，以便處理。LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM. -Main features of LIBLINEA
demosvm
- matlab编写的svm实现多类分类的源代码，训练算法包括OAA算法、OAO 算法、BSVM2算法等。-matlab prepared svm multi-category classification of the source code, training algorithms, including OAA algorithm, OAO algorithm, BSVM2 algorithm.
LibSvmUsing(lib)SVMTutorial
- 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。-I always think that SVM is a very interesting thing, however, has been no way to (mostly red hal
01imp_accord-svm-source
- The sample application is able to perform both Classification and Regression using Support Vector Machines. It can read Excel spreadsheets and determines the task to be performed depending on the number of the columns in the sheet. If the input table
svm-light
- svm分类的算法 速度比其他的快一点 需要再做比较 交流 希望能得到更多的资料-SVMmulticlass uses the multi-class formulation described in [1], but optimizes it with an algorithm that is very fast in the linear case
SVM
- C++实现的SVM程序，可以进行多类分类。-C++ implementation of the SVM procedure, can be multi-class classification.
simpleSVM_3
- 用于学习的简单的SVM程序（作者：gloosli），里面有1类SVM-The simpleSVM project contains Machine Learning codes for algorithms based on the SimpleSVM. It provides methods for Support Vector Machines and related methods, such as One-Clas SVM, nu-SVM
Recognition-activities-using-SVM
- 利用Support Vector Machine來處理對影像辨識，能判斷影像所傑取到的人是處於何種動作之下，最後並比較多種分類器之結果-Recognition of human activities using SVM multi-class classifi er,including used o-v-o,o-v-a,DAGSVM and SVM-BTA to compare.
credit-rating-using-multi-class-SVM
- 一個基於多類支援向量機的應用，將支向機應用在企業之信用評比上，能使企業得知自身所具有之優勢與劣勢，藉由改善不足之處來提升企業本身信用。-A corporate credit rating model using multi-class support vector machines to do more effective actions in performance
Multi-class-SVM-Image-Classification
- 基于神经网络的遥感图像分类取得了较好的效果,但存在固有的过学习、易陷入局部极小等缺点.支持向量机机器学习方法,根据结构风险最小化(SRM)原理,表现出很多优于其他传统方法的性能,本研究的基于多类支持向量机分类器的遥感图像分类取得了达95.4 的分类精度.但由于遥感图像分类类别多,所需训练样本较大,人工选择效率较低,为此提出以人工选择初始聚类质心、C均值模糊聚类算法自动标注训练样本的基于多类支持向量机的半监督式遥感图像分类方法,期望能在获得适用的分类精度的基础上有效提高分类效率-Neural ne
svm-yu-ce
- 神经网络SVM解决预测类的题源程序，可以作为参考-Neural network SVM to solve the question of forecasting class source, can be used as reference
one-class
- java支持向量机One-class程序，包括训练、预测文本的读取，设置好路径和文本格式，可运行-java svm one_class
SVM-class
- 这是关于svm的java源代码，带训练集，和测试集-This is about svm java source code, with training set and test set
svm-Linear-classification
- A Tutorial on Support Vector Machines for Pattern Recognition-Two Dimension Linear-SVM Problem, Two Class and Separable Situation