protected void L2RModelTraining() {
		//select the training pairs
		createTrainingCorpus();
		
		if (m_ranker==0) {
			ArrayList<Feature[]> fvs = new ArrayList<Feature[]>();
			ArrayList<Integer> labels = new ArrayList<Integer>();
			
			for(_Query q:m_queries)
				q.extractPairs4RankSVM(fvs, labels);
			Model rankSVM = SVM.libSVMTrain(fvs, labels, RankFVSize, SolverType.L2R_L1LOSS_SVC_DUAL, m_tradeoff, -1);
			
			m_weights = rankSVM.getFeatureWeights();	
			System.out.format("RankSVM training performance:\nMAP: %.4f\n", evaluate(OptimizationType.OT_MAP));

		} else if (m_ranker==1) {//all the rest use LambdaRank with different evaluator
			LambdaRank lambdaRank;
			if (m_multithread) {
				/**** multi-thread version ****/
				lambdaRank = new LambdaRankParallel(RankFVSize, m_tradeoff, m_queries, OptimizationType.OT_MAP, 10);
				lambdaRank.setSigns(getRankingFVSigns());
				lambdaRank.train(100, 100, 1.0, 0.95);//lambdaRank specific parameters
			} else {
				/**** single-thread version ****/
				lambdaRank = new LambdaRank(RankFVSize, m_tradeoff, m_queries, OptimizationType.OT_MAP);
				lambdaRank.setSigns(getRankingFVSigns());
				lambdaRank.train(300, 20, 1.0, 0.98);//lambdaRank specific parameters
			}			
			m_weights = lambdaRank.getWeights();
		} else if (m_ranker==2) {
			RankNet ranknet = new RankNet(RankFVSize, 5.0);
			ArrayList<double[]> rfvs = new ArrayList<double[]>();
			for(_Query q:m_queries)
				q.extractPairs4RankNet(rfvs);
			ranknet.setSigns(getRankingFVSigns());
			double likelihood = ranknet.train(rfvs);
			m_weights = ranknet.getWeights();
			
			System.out.format("RankNet training performance:\nlog-likelihood: %.4f\t MAP: %.4f\n", likelihood, evaluate(OptimizationType.OT_MAP));
		}		
		
		for(int i=0; i<RankFVSize; i++)
			System.out.format("%.5f ", m_weights[i]);
		System.out.println();
	}