Robust diffusion algorithms based on the maximum correntropy criterion(MCC) are developed to address the distributed networks estimation issue in impulsive(long-tailed) noise environments. The cost functions used in distributed network estimation are in general based on the mean square error (MSE) criterion, which is optimal only when the measurement noise is Gaussian. In non-Gaussian situation...

This paper introduces a new scoring criterion, factorized normalized maximum likelihood, for learning Bayesian network structures. The proposed scoring criterion requires no parameter tuning, and it is decomposable and asymptotically consistent. We compare the new scoring criterion to other scoring criteria and describe its practical implementation. Empirical tests confirm its good performance.

A central issue in dimension reduction is choosing a sensible number of dimensions to be retained. This work demonstrates the asymptotic consistency of the maximum likelihood criterion for determining the intrinsic dimension of a dataset in a isotropic version of Probabilistic Principal Component Analysis (PPCA). Numerical experiments on simulated and real datasets show that the maximum likelih...

In this paper the job shop scheduling problem with two criteria of minimizing makespan and the sum of tardiness of jobs is considered. This multi-objective problem is strongly NP-hard, as single criterion version is strongly NP-hard as well. A permutation-based representation for the job shop problem is used and a new hybrid parallel multi-agent method, called GACO (Genetic Algorithm Ant Colony...

We study a scheduling problem that belongs to the yard operations component of the railroad planning problems, namely the hump sequencing problem. The scheduling problem is characterized as a single-machine problem with stepwise tardiness cost objectives. This is a new scheduling criterion which is also relevant in the context of traditional machine scheduling problems. We produce complexity re...

In this paper, we expose a new meta-heuristic based on the artificial behavior of bee colony (ABC) for solving the blocking permutation flow shop scheduling problem (BFSP) subject to regular objectives. On three main steps, the proposed algorithm iteratively solved the BFSP under three fixed measure (makespan, total flowtime, or total tardiness criterion). Discrete job permutations and operator...

This paper describes a constraint programming approach to solving a scheduling problem with earliness and tardiness cost using a problem specific search procedure. The presented algorithm is tested on a set of randomly generated instances of the job shop scheduling problem with earliness and tardiness costs. The experiments are executed also for three other algorithms, and the results are then ...

The unrelated parallel machine scheduling (PMS) problem is essential for the manufacturing industry. Scheduling will save company resources, especially time management. By solving problems quickly and precisely, can get more profit. On that note, this paper focused on PMS problems, which did not consider inherent uncertainty in processing set up by minimizing makespan tardiness. This aimed to m...

One of the central challenges in cancer research is identifying significant genes among thousands of others on a microarray. Since preventing outbreak and progression of cancer is the ultimate goal in bioinformatics and computational biology, detection of genes that are most involved is vital and crucial. In this article, we propose a Maximum-Minimum Correntropy Criterion (MMCC) approach for se...

Gene (feature) selection has been an active research area in microarray analysis. Max-Relevance is one of the criteria which has been broadly used to find features largely correlated to the target class. However, most approximation methods for Max-Relevance do not consider joint effect of features on the target class. We propose a new MaxRelevance criterion which combines the collective impact ...