Multi-label learning deals with data associated with multiple labels simultaneously. Previous work on multi-label learning assumes that for each instance, the "full" lab...
An important drawback to the popular Belief, Desire, and Intentions (BDI) paradigm is that such systems include no element of learning from experience. We describe a novel BDI exe...
In many real world applications, the number of examples to learn from is plentiful, but we can only obtain limited information on each individual example. We study the possibiliti...
In recent years, several systems have been proposed that learn the rules of a simple card or board game solely from visual demonstration. These systems were constructed for speci...
Transfer learning proves to be effective for leveraging labeled data in the source domain to build an accurate classifier in the target domain. The basic assumption behind transf...
Mingsheng Long, Jianmin Wang 0001, Guiguang Ding, ...