Answer triggering is the task of selecting best-suited answer for a given question from set candidate answers if it exists. This paper presents hybrid deep learning model triggering, which combines several dependency graph-based alignment features, namely graph edit distance, similarity, and coverage, with dense vector embeddings Convolutional Neural Network (CNN). Our experiments on WikiQA dat...

This paper experimentally investigates the classification utility of combining features extracted from separate audio, symbolic and cultural sources of musical information. This was done via a series of genre classification experiments performed using all seven possible combinations and subsets of the three corresponding types of features. These experiments were performed using jMIR, a software...

Most of the existing facial expression recognition methods are based on either only texture features or only geometrical features. In this paper, we propose to improve the performance of facial expression recognition by combining both types of features using fuzzy integral. The geometric features used are the displacements of positions of feature points on the face. We first embed them in a low...

This paper develops a new representational model of similarity data that combines continuous dimensions with discrete features. An algorithm capable of learning these representations is described, and a Bayesian model selection approach for choosing the appropriate number of dimensions and features is developed. The approach is demonstrated on a classic data set that considers the similarities ...