搜索资源列表
newSVM
- 这是一篇改进的基于SVM多分类算法的文章,文章详细介绍了算法原理及应用,对图像图形处理专业人员的重要参考价值!-This is an improved multi-classification algorithm based on SVM article, the article describes in detail the algorithm theory and application of important reference value on the image and graphi
discrim_rf
- 斯坦福大学的姚邦鹏开发的一个图像分类算法,使用random forest实现了图像的精细区域描述,赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试,程序完全可运行,且提供了MAC和Windows下的两种程序。-Image Classification: An Integration of Randomization and Discrimination in A Dense Feature Representation The goal of our m
classification
- 分类算法,包括KNN,SVM,Linear Regression, Naive Bayes四种-four algorithms of classification, includes KNN, SVM, Linear Regression, Naive Bayes
SVM_windows
- 本源代码实现了windows下面的svm文本分类算法,对于研究数据挖掘的同学有一定的帮助。-The source code to achieve the windows below the SVM text classification algorithm for the study of data mining students have some help.
Error-driven-SVM
- 一种改进的SVM增量学习算法——基于错误驱动的增量SVM算法,改进了原有增量算法对非SV集的处理方式,将非SV集对分类信息也考虑在内,大大提高了SVM的识别进度。-An improved SVM incremental learning algorithm for- based on error-driven incremental SVM algorithm, improved the way of the the original Incremental learning algorithm
classification
- 机器学习中几种典型的分类算法,SVM, ML, Gaussian Mixture Model等-typical classifiers(SVM, ML) in ,machine learning.
svm-demo
- 一个svm的演示程序,能演示两类数据分类,有gui界面,不使用第三方工具箱,使用gaussian核函数,界面能设置c和gamma的参数值,最后可以得到分类情况的可视化效果。针对svm算法的研究者和用于教学演示的教师,是个不错的源码。-An svm demo program that can demonstrate two types of data classification, gui interface, do not use third-party toolbox, using gauss
LBPPLPQFER
- 人脸表情识别matlab程序LBP+LPQ算法融合,SVM分类-facial expression recognition algorithm fusion of LBP and LPQ,clssify by OAA SVM
Adaboost
- matlab实现AdaBoost,弱分类算法包括fisher 伪逆 svm naivebayes 决策树。-matlab implements AdaBoost, weak classification algorithms include fisher pseudoinverse svm naivebayes tree.
SVM
- 运用支持向量机作为分类器,适用于特征选择算法-Using support vector machine as classifier, feature selection algorithm
Ttest-RFE-SVM
- 对DNA微阵列用RFE-SVM算法对基因进行分类-Use of DNA microarray gene RFE-SVM algorithm to classify
Mycold
- 在matlab软件中,自己编程实现的一个分类算法,算法首先采用kmeans与fcm聚类分析方法进行采样,然后利用svm对选取得样本进行分类-In matlab software, own programming to achieve a classification algorithm, the algorithm first used fcm kmeans cluster analysis methods and sampling, and then use the elections to
svm-matlab
- 支持向量机(SVM)用于分类的算法实现 function [D, a_star] = SVM(train_features, train_targets, params, region)-Support Vector Machine (SVM) algorithm for classification in function [D, a_star] = SVM (train_features, train_targets, params, region)
svm-examples-code-
- svm 算法源码 分类实例,vc版本的,可以直接运行dsw工程-svm classify source code
SVM-reviewed
- 支持向量机方法中也存在着一些亟待解决的问题,主要包括:如何用支持向量机更有效的解决多类分类问题,如何解决支持向量机二次规划过程中存在的瓶颈问题、如何确定核函数以及最优的核参数以保证算法的有效性等。-Support vector machine (SVM) method also exist some problems to be solved, mainly includes: how to use support vector machine (SVM) is more effective t
Code
- SVM、Adaboost、K-Means 分类算法的实现,有助于理解分类算法的原理。-SVM, Adaboost, K-Means classification algorithm, helps to understand the principles of classification algorithms.
SVM
- SVM工具箱,各种分类及拟合算法,使用起来很方便。-SVM tools for classification and approximation
SVM
- 支持向量机,用于各种分类和回归,是一种比较常用的智能算法-SVM for regression and classification
SVM
- SVM: 一种分类器,采用最大化分类间隔进行优化参数。 关于这个分类器两点比较重要: 1)SMO优化算法需要掌握, 可以具体参看两篇文章,John Platt的文章 以及“Improvements to Platt s SMO algorithm for SVM Classifier Design” 2)核函数的使用,如何将核函数使用到SVM中,核函数就是空间转换的函数, 说白了就是距离计算函数,如何将同类之间的距离计算的比较近,如何将低维空间转换到易于分类的高维空间
LBPSVM
- 实现了基于LBP和支持向量机的分类算法。首先提取出LBP特征,再利用支持向量机进行分类-Realized based on LBP and support vector machine (SVM) classification algorithm.First extract LBP feature, using support vector machine (SVM) classification