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SVM多分类算法，基于svmlib适合初学者学习(SVM multi classification algorithm, based on svmlib suitable for beginners to learn)
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输入训练样本集和测试样本集，通过提取HOG然后用SVM实现分类。(Input training samples and test samples, extract HOG and implement classification with SVM.)
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实现分类,回归的算法，可以直接下载运行验证(Implementation of classification, regression algorithm, can be directly downloaded operation verification)
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调用于sklearn平台的支持向量机算法，有着较好的分类能力(The support vector machine algorithm for sklearn platform has good classification ability)
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支持svm多分类，运算时间较长，支持svm多分类的matlab代码，精度不高。(Support svm multi-classification)
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语音情感识别分类，在中科大录制的语音情感数据库CASIA中来实现的(Speech emotion recognition and classification is implemented in CASIA, a speech emotion database recorded by China University of science and technology.)
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读取地震数据并进行SVM训练分类，针对特殊数据进行训练。输入：坐标，输出：标签(Read seismic data and conduct SVM training classification, training for special data. Input: coordinate, output: Label)
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svm分类训练器，台湾大学教授编写的。功能强大，可以试一试。欢迎大家下载，哈哈(SVM classification training device, written by National Taiwan University professor. It is powerful and can be tried. Welcome to download, haha)
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在matlab平台上使用SVM对iris数据集进行分类(use SVM Classification of Iris data set in matlab)
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采用matlab机器学习语言，支持向量机(Machine learning language classification)
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一般的支持向量机只支持二分类，使用libsvm可以实现多分类，原理也是基于二分类，然后在使用投票机制，经测验，libsvm的分类精度可达85%以上(Multi class supported by libsvm,after testing, the classification accuracy can reach 85%.)
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算法功能是SVM分类，使用PCA降维处理，一个文件是直接分类，另一个是降维后分类(Classification using SVM algorithm)
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数据单一分类，svm和lstm两种，可以简单用于测试(data classification)
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PSO-LSSVM分类测试程序代码模板,PSO优化LSSVM工具箱——分类部分(PSO-LSSVM Classification Test Program Code Template, PSO Optimizing LSSVM Toolbox-Classification Part)
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DE+SVM的简单实现，通过准确率来优化SVM的参数，从而提高分类准确率(The simple implementation of DE+SVM optimizes the parameters of SVM through the accuracy, so as to improve the classification accuracy)
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利用原算法adaboost弱学习器基于决策树桩的方法对样本数据进行多分类(Multi-classification of sample data based on decision tree stump using AdaBoost weak learner)
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利用遗传算法、蚁群算法、PSO等对SVM模型进行优化，实现高效分类和回归预测(The SVM model is optimized by genetic algorithm, ant colony algorithm and PSO to achieve efficient classification and regression prediction.)
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针对“BreastCancer”数据集，作为对比，第一次对特征集直接进行SVM分类，第二次使用粒子群算法进行特征选择后再进行SVM分类。并且对比和分析了两次分类的结果。(For "BreastCancer" data set, as a comparison, the first time the feature set is directly classified by SVM, and the second time the feature set is selected
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多分类孪生支持向量机,主体是-1 1的2分类孪生支持向量机，采用onevsone改编成多分类的孪生支持向量机(multi classification twin support vector machine, kernel code is binary-classification twin support vector machine ,constructed it as a multi classification twin support vector machine by using O
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SVM的分类与预测。带数据的。可直接使用学习。方便入门。(Classification and prediction of SVM. With data. You can use learning directly.it's convence for us to learn it .)
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