The objective and automated monitoring of depression using behavioral signals is confounded by the wide clinical profile of this commonly occurring mood disorder. This paper introduces Relevance Vector Machines, a novel method for predicting clinical depression scores from paralinguistic cues. It highlights many of the advantages RVM can offer depression prediction; sparsity, implicit noise cha...

We propose a method to automatically determine the relevance of the input dimensions of a learning vector quantization (LVQ) architecture during training. The method is based on Hebbian learning and introduces weighting factors of the input dimensions which are automatically adapted to the speci c problem. The bene ts are twofold: On the one hand, the incorporation of relevance factors in the L...

Abstract Many researchers have shown interest in profile monitoring; however, most of the applications this field research are developed under assumption normal response variable. Little attention has been given to monitoring with non-normal variables, known as general linear models which consists two main categories (i.e., logistic and Poisson profiles). This paper aims monitor problem Phase I...

This paper describes and evaluates a retrieval scheme, or more precisely an additional retrieval mechanism based on interdocument relationships, that can be integrated in almost all existing retrieval schemes (e.g., Boolean, hybrid Boolean, vector-processing or probabilistic models). The intent of our approach consists of inferring knowledge about document contents based on the relevance assess...