In recent years, matrix approximation for missing value prediction has emerged as an important problem in a variety of domains such as recommendation systems, e-commerce and onlin...
Covariate shift is a situation in supervised learning where training and test inputs follow different distributions even though the functional relation remains unchanged. A common...
Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen...
This paper presents a stagewise least square (SLS) loss function for classification. It uses a least square form within each stage to approximate a bounded monotonic nonconvex los...
Recently, modern tracking methods started to allow capturing the position of massive numbers of moving objects. Given this information, it is possible to analyze and predict the t...
Although clustering under constraints is a current research topic, a hierarchical setting, in which a hierarchy of clusters is the goal, is usually not considered. This paper trie...