In this paper a variable neighborhood search approach as a method for solving combinatory optimization problems is presented. A variable neighborhood search based algorithm for solving the problem concerning the university course timetable design has been developed. This algorithm is used to solve the real problem regarding the university course timetable design. It is compared with other algor...

In data mining classification is to accurately predict the target class for each case in the data. Decision tree algorithm is one of the commonly used classification algorithm to make induction learning based on examples. In this paper we present the comparison of different classification techniques using WEKA. The aim of this paper is to investigate the performance of different classification ...

Naive Bayes is an eecient and eeective learning algorithm, but previous results show that its representation ability is severely limited since it can only represent certain linearly separable functions in the binary domain. We give necessary and suucient conditions on linearly separable functions in the binary domain to be learnable by Naive Bayes under uniform representation. We then show that...

We design a new learning algorithm for the Set Covering Machine from a PAC-Bayes perspective and propose a PAC-Bayes risk bound which is minimized for classifiers achieving a non trivial margin-sparsity trade-off.