Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers

抄録

Probabilistic classification and multi-task learning are two important branches of machine learning research. Probabilistic classification is useful when the ‘confidence’ of decision is necessary. On the other hand, the idea of multi-task learning is beneficial if multiple related learning tasks exist. So far, kernelized logistic regression has been a vital probabilistic classifier for the use in multi-task learning scenarios. However, its training tends to be computationally expensive, which prevented its use in large-scale problems. To overcome this limitation, we propose to employ a recently-proposed probabilistic classifier called the least-squares probabilistic classifier in multi-task learning scenarios. Through image classification experiments, we show that our method achieves comparable classification performance to the existing method, with much less training time.