搜索资源列表
svm_java
- 应用java实现的svm支持向量机的分类算法！相信对svm感兴趣的朋友有很大帮助！-java application realized svm SVM classification algorithm! Believe that the right svm interested friends will be of great help!
svm_c++
- 应用c＋＋实现的svm向量机分类算法，相信对svm感兴趣的朋友有帮助！-c + + applications to achieve the svm vector machine classification algorithm, I believe right svm interested friends!
3SVM
- 支持向量机算法 程序 用于解决支持向量机 数据挖掘和段数据训练-SVM procedure for solving support vector machines in data mining and training section
MATLAB-NN
- MATLAB神经网络的各类程序，包含遗传算法，神经网络，神经模糊，SVM等程序，十分实用。-All kinds of MATLAB neural network program, including genetic algorithms, neural networks, neuro-fuzzy, SVM and other procedures, very useful.
XIAOBOsvm
- 基于小波分析、遗传算法、支持向量机对股指期货进行预测的算法。-Based on the wavelet analysis, genetic algorithms, the algorithm of support vector machine (SVM) to forecast the stock index futures.
SVM-huiguisuanfa
- 运用支持向量机算SVM法进行回归拟合计算，很好用的算法-Support vector machine SVM method to calculate regression calculation, the good algorithm
svmMlIA
- python编写的svm算法，清晰易懂，并且附有源数据文件-written python svm algorithm, clear and easy to understand, and with the source data file
SVM-judging-whether-wearing-glasses-
- 人脸识别，基于svm算法检测人脸是否配戴眼镜-Face recognition, face detection algorithm based on svm whether to wear glasses
PSO-SVM
- 粒子群优化算法优化支持向量机（PSO-SVM)-Particle swarm optimization algorithm optimize the SVM
chapter15_PSO
- svm 的参数优化，利用pso(粒子群优化算法)选择最优参数c g，最终提高训练集的分类准确率，更好的提高分类器性能-Svm parameter optimization, the use of pso (particle swarm optimization algorithm) to the optimal parameter c g, and ultimately improve the training set classification accuracy, better impr
chapter15_GA
- svm 的参数优化，利用ga(遗传优化算法)选择最优参数c g，最终提高训练集的分类准确率，更好的提高分类器性能-Svm parameter optimization, the use of ga (genetic optimization algorithm) to the optimal parameter c g, and ultimately improve the accuracy of the training set classification, better improve
gaSVMcgForClass
- svm 的参数优化，利用ga(遗传优化算法)选择最优参数c g，最终提高训练集的分类准确率，更好的提高分类器性能,这是ga的功能函数源码-Svm parameter optimization, the use of ga (genetic optimization algorithm) to the optimal parameter c g, and ultimately improve the training set classification accuracy, better imp