搜索资源列表
TinySvmTest
- 用于汉字识别和分类的支持向量机SVMTEST测试算法,很好用啊-for Chinese character recognition and classification of SVM SVMTEST test algorithm is useful ah
SvmTest
- 利用VC6实现的SVM分类器的源代码，可以用来分类或识别
TinySvmTest
- 用于汉字识别和分类的支持向量机SVMTEST测试算法,很好用啊-for Chinese character recognition and classification of SVM SVMTEST test algorithm is useful ah
SvmTest
- 利用VC6实现的SVM分类器的源代码，可以用来分类或识别-VC6 realize the use of the SVM classifier the source code can be used to classification or identification
svmtest
- 利用svm结合向量机实现蛋白质相互作用预测-Svm vector machine achieved with the use of protein-protein interaction prediction
svmTest
- SVM 在JAVA中的运用，简单的DEMO测试，用于做图像识别分析。支援向量机源码调用实例里面有测试资料供简单测试。-The use of SVM in JAVA, simple DEMO test, used to make image recognition analysis. Support vector machine called an instance of source test data for which there
SVMTest
- 用于识别样本类别的支持向量机算法，该算法使用matlab实现。-Sample used to identify categories of support vector machine algorithm using matlab implementation.
SVMtest
- SVM算法的例子和简单说明，可以用于学习SVM算法。-SVM algorithm examples and simple instructions, can be used for SVM learning algorithm.
svmtest
- 支持向量机，像神经网络一样能够将数据进行分类-Support vector machines, data classification.
svmtest
- 简单地支持向量机的测试程序，希望对你们有用,它是测试两个线性分类的。-A simple test program to support vector machine, and I hope useful to you.
SVMtest
- 经测试数据验证 可以实现支持向量机的分类，该算法使用C++编写-Verified by the test data of support vector machine (SVM) classification, the algorithm using c++
svmTest
- Opencv300中的SVM，进行训练和分类，一个简单的范例，知道配置和使用就行了，抛砖引玉的作用-Opencv300 SVM, for training and classification,, a simple example is, know to configure and use, a valuable role
Svmtest
- DBN算法java实现，网上很难找到java实现的代码-DBN algorithm to achieve the java code is difficult to find online java implementation
SVMTest
- 支持向量机算法实现,Support vector machine algorithm implementation.-Support vector machine algorithm implementation
SVMtest
- 调用C++ LIBSVM库实现SVM支持向量机模型的训练以及预测，输入输出均为CSV格式的文件。-Call the C++ LIBSVM library to achieve SVM support vector machine model training and forecasting, input and output are CSV format files.
svm
- SVM ： /kernel.m /main.m /svmTest /svmTrain.m 亲测可用，直接运行main函数-SVM ： /kernel.m /main.m /svmTest /svmTrain.m