Accurately identifying corresponded landmarks from a
population of shape instances is the major challenge in
constructing statistical shape models. In general, shapecorrespondenc...
Traditional approaches to Multiple-Instance Learning (MIL) operate under the assumption that the instances of a bag are generated independently, and therefore typically learn an in...
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. In this paper, we propose an approach based on On...
Chengcui Zhang, Xin Chen, Min Chen, Shu-Ching Chen...
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support ...
Today's query processing engines do not take advantage of the multiple occurrences of a relation in a query to improve performance. Instead, each instance is treated as a dis...
Yu Cao, Gopal C. Das, Chee Yong Chan, Kian-Lee Tan