There are a variety of methods for inducing predictive systems from observed data. Many of these methods fall into the field of study of machine learning. Some of the most effec...
In this work, we study the problem of within-network relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to ...
This paper aims at discovering community structure in rich media social networks, through analysis of time-varying, multi-relational data. Community structure represents the laten...
Yu-Ru Lin, Jimeng Sun, Paul Castro, Ravi B. Konuru...
Data Mining with Bayesian Network learning has two important characteristics: under broad conditions learned edges between variables correspond to causal influences, and second, f...
Ioannis Tsamardinos, Constantin F. Aliferis, Alexa...
Value Prediction is a relatively new technique to increase instruction-level parallelism by breaking true data dependence chains. A value prediction architecture produces values, ...