線性SVM的原理就不多說了，最強大的就是libsvm庫（ml庫也是用這個），參考：http://blog.csdn.net/fjssharpsword/article/details/53883340

這里直接給出ML庫的示例代碼：

/*** This file is part of the Java Machine Learning Library* * The Java Machine Learning Library is free software; you can redistribute it and/or modify* it under the terms of the GNU General Public License as published by* the Free Software Foundation; either version 2 of the License, or* (at your option) any later version.* * The Java Machine Learning Library is distributed in the hope that it will be useful,* but WITHOUT ANY WARRANTY; without even the implied warranty of* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the* GNU General Public License for more details.* * You should have received a copy of the GNU General Public License* along with the Java Machine Learning Library; if not, write to the Free Software* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA* * Copyright (c) 2006-2012, Thomas Abeel* * Project: http://java-ml.sourceforge.net/* */ package com.gddx;import java.io.File;import libsvm.SelfOptimizingLinearLibSVM; import net.sf.javaml.classification.Classifier; import net.sf.javaml.core.Dataset; import net.sf.javaml.core.Instance; import net.sf.javaml.tools.data.FileHandler;/*** This tutorial show how to use a the LibSVM classifier.* * @author Thomas Abeel* */ public class TutorialSelfOptimizingLibSVM {/*** Shows the default usage of the LibSVM algorithm.*/public static void main(String[] args) throws Exception {/* Load a data set */Dataset data = FileHandler.loadDataset(new File("D:\\tmp\\javaml-0.1.7-src\\UCI-small\\iris\\iris.data"), 4, ",");/** Contruct a LibSVM classifier with default settings.*/Classifier svm = new SelfOptimizingLinearLibSVM();svm.buildClassifier(data);/** Load a data set, this can be a different one, but we will use the* same one.*/Dataset dataForClassification = FileHandler.loadDataset(new File("D:\\tmp\\javaml-0.1.7-src\\UCI-small\\iris\\iris.data"), 4, ",");/* Counters for correct and wrong predictions. */int correct = 0, wrong = 0;/* Classify all instances and check with the correct class values */for (Instance inst : dataForClassification) {Object predictedClassValue = svm.classify(inst);Object realClassValue = inst.classValue();if (predictedClassValue.equals(realClassValue))correct++;elsewrong++;}System.out.println("Correct predictions " + correct);System.out.println("Wrong predictions " + wrong);}}
發(fā)現(xiàn)一個linearsvm的網(wǎng)站http://www.linearsvm.com/，說可以處理超大數(shù)據(jù)集，可以試驗下。

對ML庫的序列學(xué)習(xí)就基本到此，總結(jié)三點：

1）還是python scikit-learn好用，其次是spark mlib庫，java ml還是有所缺的；

2）Java ML庫的源碼可以繼續(xù)研究，對于機器學(xué)習(xí)庫主要就是三個部分：數(shù)據(jù)處理、方法實現(xiàn)、模型評價；

3）Java ML庫的API可以參考：http://java-ml.sourceforge.net/api/0.1.7/，但是這個說明文檔實在是過于簡單。

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