更多代码请见：https://github.com/xubo245/SparkLearning
Spark中组件Mllib的学习之分类篇
1解释

 支持向量机(Support Vector Machine，SVM)是Corinna Cortes和Vapnik等于1995年首先提出的，它在解决小样本、非线性及高维模式识别中表现出许多特有的优势，并能够推广应用到函数拟合等其他机器学习问题中。

SVM的概念在机器学习的书上讲的比较清楚，博客请见【4】

2.代码：

/**
  * @author xubo
  *         ref:Spark MlLib机器学习实战
  *         more code:https://github.com/xubo245/SparkLearning
  *         more blog:http://blog.csdn.net/xubo245
  */
package org.apache.spark.mllib.learning.regression

import java.text.SimpleDateFormat
import java.util.Date

import org.apache.spark.mllib.classification.{SVMModel, SVMWithSGD}
import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.{SparkConf, SparkContext}

/**
  * Created by xubo on 2016/5/23.
  * SVM
  */
object SVMFromSparkLearning {
  def main(args: Array[String]) {
    val conf = new SparkConf().setMaster("local[4]").setAppName(this.getClass().getSimpleName().filter(!_.equals('$')))
    val sc = new SparkContext(conf)

    // Load training data in LIBSVM format.
    val data = MLUtils.loadLibSVMFile(sc, "file/data/mllib/input/regression/sample_libsvm_data.txt")

    // Split data into training (60%) and test (40%).
    val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
    val training = splits(0).cache()
    val test = splits(1)

    // Run training algorithm to build the model
    val numIterations = 100
    val model = SVMWithSGD.train(training, numIterations)

    // Clear the default threshold.
    model.clearThreshold()

    // Compute raw scores on the test set.
    val scoreAndLabels = test.map { point =>
      val score = model.predict(point.features)
      (score, point.label)
    }

    // Get evaluation metrics.
    val metrics = new BinaryClassificationMetrics(scoreAndLabels)
    val auROC = metrics.areaUnderROC()

    println("Area under ROC = " + auROC)
    println(model.weights)
    println("model.weights.size"+model.weights.size)
    scoreAndLabels.take(10).foreach(println)
    // Save and load model
    val iString = new SimpleDateFormat("yyyyMMddHHmmssSSS").format(new Date())
    val path = "file/data/mllib/output/regression/sample_libsvm_data" + iString + "/result"
    model.save(sc, path)
    val sameModel = SVMModel.load(sc, path)
    println(sameModel.weights)
    sc.stop
  }
}

3.结果：

Area under ROC = 1.0
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model.weights.size692
(-893656.2467488575,0.0)
(553216.1154887225,1.0)
(-1030793.6628224523,0.0)
(-660018.4363039621,0.0)
(473494.1478776787,1.0)
(474484.8472541839,1.0)
(625896.1173054989,1.0)
(-1179877.5567907898,0.0)
(459173.33117972035,1.0)
(547268.1947242465,1.0)

后面model.save和load好慢，就没等了

参考
【1】http://spark.apache.org/docs/1.5.2/mllib-guide.html
【2】http://spark.apache.org/docs/1.5.2/programming-guide.html
【3】https://github.com/xubo245/SparkLearning
【4】http://blog.csdn.net/macyang/article/details/38782399