We show how the Full Bayesian Signi cance Test (FBST) can be used as a model selection criterion. The FBST was presented by Pereira and Stern [3842] as a coherent Bayesian signi cance test.

We study a variant of the Whitney extension problem [21, 22] for the space C k,ω (R n). We identify C k,ω (R n) with a space of Lipschitz mappings from R n into the space P k × R n of polynomial fields on R n equipped with a certain metric. This identification allows us to reformulate the Whitney problem for C k,ω (R n) as a Lip-schitz selection problem for set-valued mappings into a certain fa...

abstract. today, there is no specific method or criterion for selecting contractors in the researches’ field. usually this selection is regarded as a matter of taste and as a result, there is no coordinated approach in the mentioned domain. sometimes, after signing the contract, the inappropriate selection became clear. the managers are in direct need of a pattern for selecting the contractors....

Fractional factorial designs are used widely in screening experiments to identify significant effects. It is not always possible to perform the trials in a complete random order and hence, fractional factorial split-plot designs arise. In order to identify optimal fractional factorial split-plot designs in this setting, the Hellinger distance criterion (Bingham and Chipman (2007)) is adapted. T...

In addition to accuracy, stability, having not too significant changes in the selected features when the identity of samples change, is also a measure of success for a feature selection algorithm. Stability could especially be a concern when the number of samples in a data set is small and the dimensionality is high. In this study, we introduce a stability measure that can be used for the case ...

Many machine learning algorithms can be formulated as the minimization of a train ing criterion which involves training errors on each training example and some hyper parameters which are kept xed during this minimization When there is only a single hyper parameter one can easily explore how its value a ects a model selection criterion that is not the same as the training criterion and is used ...

The Optimally Pruned Extreme Learning Machine (OPELM) and Optimally Pruned K-Nearest Neighbors (OP-KNN) algorithms use the a similar methodology based on random initialization (OP-ELM) or KNN initialization (OP-KNN) of a Feedforward Neural Network followed by ranking of the neurons; ranking is used to determine the best combination to retain. This is achieved by Leave-One-Out (LOO) crossvalidat...

This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (LSSVM). Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. The fault diagnosis method co...

The paper presents an original filter approach for effective feature selection in classification tasks with a very large number of input variables. The approach is based on the use of a new information theoretic selection criterion: the double input symmetrical relevance (DISR). The rationale of the criterion is that a set of variables can return an information on the output class that is highe...

Differential Evolution (DE) is one of the most successful and powerful evolutionary algorithms for global optimization problem. The most important operator in this algorithm is mutation operator which parents are selected randomly to participate in it. Recently, numerous papers are tried to make this operator more intelligent by selection of parents for mutation intelligently. The intelligent s...