A probabilistic kernel approach for solving the multi-instance learning problems with different assumptions

Abstract

Multi-instance learning (MIL) has received more and more attentions in the machine learning research field due to its theoretical interest and its applicability to diverse real-world problems. In this paper, we present a probabilistic kernel approach for the multi-instance learning problems with various multi-instance assumptions by imposing Gaussian process prior on an unobservable latent function defined on the instance space. Because the relationship between the bag and its instances, triggered by the multi-instance assumption, can be exactly captured by defining the likelihood function, we can deal with different multi-instance assumptions by employing different likelihood functions. Experimental results on several multi-instance problems show that the proposed algorithms are valid and can achieve superior performance to the published MIL algorithms.