in this paper, the performance of 11 different distances for image retrieval and classification, based on color, shape and texture, is evaluated. the precision-recall measure and the correct classification rate of the k-nn classifier are used to evaluate retrieval and classification performances, respectively. the experimental results for a database of 1000 images from 10 different semantic gro...

in this paper, the performance of 11 different distances for image retrieval and classification, based on color, shape and texture, is evaluated. the precision-recall measure and the correct classification rate of the k-nn classifier are used to evaluate retrieval and classification performances, respectively. the experimental results for a database of 1000 images from 10 different semantic gro...

This paper describes two practical fusion techniques (hybrid fusion and cued fusion) for automatic target cueing that combine features derived from each sensor data at the object-level. In the hybrid fusion method each of the input sensor data is prescreened (i.e. Automatic Target Cueing (ATC) is performed) before the fusion stage. The cued fusion method assumes that one of the sensors is desig...

Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, whilst test data is sampled from a target domain. This paper proposes and studies an approach, called feature-level domain adaptation (flda), that models the dependence between the two domains by means of a feature-leve...

Natural language understanding (NLU) models often rely on dataset biases rather than intended task-relevant features to achieve high performance specific datasets. As a result, these perform poorly datasets outside the training distribution. Some recent studies address this issue by reducing weights of biased samples during process. However, methods still encode latent in representations and ne...

Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation accuracy and ease in online penalization thus is favored by industrial recommender systems. ICF recommends items to a target user based on their similarities previously interacted user. Great progresses have been achieved for recent years applying advanced machine learning techniques (e.g., deep neural network...