This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
This paper proposes the applications of soft computing to deal with the constraints in conventional modelling techniques of the dynamic extrusion process. The proposed technique i...
Leong Ping Tan, Ahmad Lotfi, Eugene Lai, J. B. Hul...
Markov models have been widely utilized for modelling user web navigation behaviour. In this work we propose a dynamic clustering-based method to increase a Markov model's ac...
We study the problem of minimizing a sum of p-norms where p is a fixed real number in the interval [1, ]. Several practical algorithms have been proposed to solve this problem. How...
Abstract. Real life scheduling problems are solved by heuristics with parameters defined by experts, as usual. In this paper a new approach is proposed where the parameters of vari...