Sentence examples for mutual information criterion from inspiring English sources

Mutual information criterion is found to be useful for the ANNs for the study area.

The mutual information criterion may be used in that context, but it suffers from the difficulty of its estimation through a finite set of samples.

The synaptic weights of the neural networks produced by the constructive mechanism are adjusted by a quasi-Newton method, and the decision to grow or prune the current network is based on a mutual information criterion.

The mutual information criterion is adopted to select the powerful features from ones generated from the above-mentioned four texture analysis methods in the training stage, meanwhile, the five implementations of multi-class support vector machine classifiers are also designed to discriminate each image into one of the four disease groups in the classification stage.

Application of the mutual information criterion for feature selection in computer-aided diagnosis.

The overlap in communities, measured through a normalized mutual information criterion, is around 90% for all collaboration networks.

(3) A mutual information criterion-based feature selection is investigated for the MC detection in FFDM, and until now, only very few MC detection works have been done on the FFDM dataset.

We selected the best of these features using the approach proposed by Peng et al. [16]; the method is an optimal first-order approximation of the mutual information criteria.

The anatomical image was co-registered to the mean functional image using the mutual information criteria method and segmented and normalized to the International Consortium for Brain Mapping template using linear and non-linear deformations [30], [31].

Bayesian networks [ 68- 70], Boolean networks [ 71- 73], and mutual information criteria [ 69, 74- 76] have all been successfully used in similar situations, and the analysis methods described here should be compatible with all of these methods.

The GMM-HMM baseline system has 40-Gaussian mixtures per state, trained with maximum likelihood and refined discriminatively with the boosted maximum-mutual-information criterion.