This paper examines important factors for link prediction in networks and provides a general, high-performance framework for the prediction task. Link prediction in sparse network...
Ryan Lichtenwalter, Jake T. Lussier, Nitesh V. Cha...
Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covaria...
In many applications, unlabelled examples are inexpensive and easy to obtain. Semisupervised approaches try to utilise such examples to reduce the predictive error. In this paper,...
We propose a new multiple instance learning (MIL) algorithm to learn image categories. Unlike existing MIL algorithms, in which the individual instances in a bag are assumed to be...
Guo-Jun Qi, Xian-Sheng Hua, Yong Rui, Tao Mei, Jin...
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...