Almost all the approaches in association rule mining suggested the use of a single minimum support, technique that either rules out all infrequent itemsets or suffers from the bottleneck of generating and examining too many candidate large itemsets. In this paper we consider the combination of two well-known algorithms, namely algorithm DIC and MSApriori in order to end up with a more effective...

The property of decentralized integral controllability (DIC) is related to the concept of passivity in this note. A su$cient condition for DIC is proposed based on the passivity theorem and a computational method for testing DIC is presented. 2001 Elsevier Science Ltd. All rights reserved.

Prior design is one of the most important problems in both statistics and machine learning. The cross validation (CV) and the widely applicable information criterion (WAIC) are predictive measures of the Bayesian estimation, however, it has been difficult to apply them to find the optimal prior because their mathematical properties in prior evaluation have been unknown and the region of the hyp...

. This paper proposes a new complexity-penalization model selection strategy derived from the minimum risk principle and the behavior of candidate models under noisy conditions. This strategy seems to be robust in small sample size conditions and tends to AIC criterion as sample size grows up. The simulation study at the end of the paper will show that the proposed criterion is extremely compet...

This paper discusses the use of Linear Mixed Models (LMM) and Generalized Linear Mixed Models (GLMM) to predict the wear and damage trajectories of railway wheelsets for a fleet of modern multiple unit trains. The wear trajectory is described by the evolution of the wheel flange thickness, the flange height and the tread diameter; whereas the damage trajectory is assessed through the probabilit...

It is shown in this paper that the data augmentation technique undermines the theoretical underpinnings of the deviance information criterion (DIC), a widely used information criterion for Bayesian model comparison, although it facilitates parameter estimation for latent variable models via Markov chain Monte Carlo (MCMC) simulation. Data augmentation makes the likelihood function non-regular a...

We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure pD for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general pD approximately c...

This paper identifies two novel techniques for face features extraction based on two different multi-resolution analysis tools; the first called curvelet transform while the second is waveatom transform. The resultant features are trained and tested via three improved hidden Markov Model (HMM) classifiers, such as: Structural HMM (SHMM), Deviance Information CriterionInverse Weighted Average K-...

A general information criterion with a general penalty which depends on the size of samples is developed for nested and non-nested models in the context of inequality constraints. The true parameters may be defined by a specified parametric model, or a set of specified estimating functions. When the true parameters are defined by estimating functions, we use the empirical likelihood approach to...