l1-Norm support vector machine for ranking with exponentially strongly mixing sequence

The problem of ranking/ordering instances, instead of simply classifying them, has recently gained much attention in machine learning. Ranking from binary comparisons is a ubiquitous problem in modern machine learning applications. In this paper, we consider l(1)-norm SVM for ranking. As well known, learning with l(1)-norm restrictions usually leads to sparsity. Moreover, instead of independently draw sample sequence, we are given sample of exponentially strongly mixing sequence. Under some mild conditions, a learning rate is established.