Simple example for a persistent classifier model in Weka (http://www.cs.waikato.ac.nz/ml/weka/). The Decision Tree (J48) can be exchanged with an arbitrary classifier.

WekaSaveClassifier.java

//package ...;

import java.io.BufferedReader;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.IOException;

import weka.classifiers.Classifier;
import weka.classifiers.trees.J48;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;

public class WekaSaveClassifier {

	protected final static String DATA_PATH = "data/unbalanced.arff";
	protected final static String CLASSIFIER_PATH = "save/cls.model";

	public static void main(String[] args) {
		// Source: http://weka.wikispaces.com/Serialization
		try {
			new WekaSaveClassifier(DATA_PATH, CLASSIFIER_PATH);
		} catch (Exception e) {
			e.printStackTrace();
		}
	}

	public WekaSaveClassifier(String dataPath, String classifierPath) throws Exception {

		System.out.println("[LOG]\tget classifier");
		Classifier cls = new J48();

		System.out.println("[LOG]\tload dataset");
		Instances instDataset = null;
		instDataset = loadData(dataPath);

		System.out.println("[LOG]\ttrain classifier with dataset");
		cls.buildClassifier(instDataset);
		
		// new unknown instance
		Instance newInst = getUserInput(instDataset);
		
		System.out.println(
			"[LOG]\tclassification before saving the cls: " + 
			cls.classifyInstance(newInst)
		);

		System.out.println("[LOG]\tserialize model:\t" + classifierPath);
		saveClassifier(cls, classifierPath);

		System.out.println("[LOG]\tdeserialize model:\t" + classifierPath);
		Classifier c = loadClassifier(classifierPath);
		if (null != c)
			cls = c;
		else
			System.err.println("Could not load classifier from " + classifierPath);
		
		System.out.println(
			"[LOG]\tclassification after reloading the cls: " + 
			cls.classifyInstance(newInst)
		);
	}

	/**
	 * This class collects user input.
	 * Currently random/dummy input is generated for demonstration
	 * @param dataset the dataset (i.e. from an .arff file) holding the new instances feature vector structure
	 * @return the new instance given by the user
	 */
	private Instance getUserInput(final Instances dataset) {
		//dataset.firstInstance().numAttributes() == 33 // -1 class
		Instance in = new DenseInstance(33);
		in.setDataset(dataset);
		in.setClassMissing(); // not necessary
		for(int i=0; i<32; ++i)
			in.setValue(i, -2.5d + Math.random()*5.0d);
		//System.out.println(in);
		return in;
	}

	protected Instances loadData(final String path)
			throws FileNotFoundException, IOException {
		Instances inst = new Instances(new BufferedReader(new FileReader(path)));
		inst.setClassIndex(inst.numAttributes() - 1);
		return inst;
	}

	protected void saveClassifier(final Classifier cls, final String path)
			throws Exception {
		weka.core.SerializationHelper.write(path, cls);
	}

	protected Classifier loadClassifier(String classifierPath) {
		Classifier cls = null;
		try {
			cls = (Classifier) weka.core.SerializationHelper
					.read(classifierPath);
		} catch (Exception e) {
			e.printStackTrace();
		}
		return cls;
	}

}