The amygdala is critically involved in discriminative avoidance learning. Large lesions of the amygdala block discriminative avoidance learning and abolish cingulothalamic training-induced neuronal activity. These results indicated that amygdalar processing is critical for cingulothalamic plasticity. The larger lesions did not allow differentiation of the specific functioning of various amygdal...

Semi-supervised speaker clustering refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. In the form of an independent training set, the prior knowledge helps us learn a speaker-discriminative feature transformation, a universal speaker prior model, and a discriminative speaker subspace, or equivalently a speaker-discriminative di...

We discuss the evaluation of subsets of variables for the discriminative evidence they provide in multivariate mixture modeling for classification. Novel development of Bayesian classification analysis uses a natural measure of concordance between mixture component densities, and defines an effective and computationally feasible method for assessing and prioritizing subsets of variables accordi...

Gene selection is usually the crucial first step in microarray data analysis. One class of typical approaches is to calculate some discriminative scores using data associated with a single gene. Such discriminative scores are then sorted and top ranked genes are selected for further analysis. However, such an approach will result in redundant gene set since it ignores the complex relationships ...

Abstract In this paper, discriminative associative classification is proposed as a new technique based on class association rules (CDARs). These are defined itemsets. The itemset frequent in one data and has much higher frequencies compared with the same other classes. CDAR rule (CAR) that support Compared to classification, there additional challenges Apriori property of subset not applicable....

We discuss the evaluation of subsets of variables for the discriminative evidence they provide in multivariate mixture modeling for classification. The novel development of Bayesian classification analysis presented is partly motivated by problems of design and selection of variables in biomolecular studies, particularly involving widely used assays of large-scale single-cell data generated usi...

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...

Distance-based indices, including closeness centrality, average path length, eccentricity and average eccentricity, are important tools for network analysis. In these indices, the distance between two vertices is measured by the size of shortest paths between them. However, this measure has shortcomings. A well-studied shortcoming is that extending it to disconnected graphs (and also directed g...