An efficient framework for fuzzy classifiers – We propose a robust approach for fuzzy classifiers with a limited number of instances that learns to reason about (i.e. the classifiers). The proposed approach consists of three steps. First, we consider each instance separately and make the optimal decision for each instance. Second, we consider the probability of each instance to be a fuzzy probability vector, and perform a Bayesian search to identify the most informative fuzzy classifier. Finally, we perform two Bayesian optimization steps, one for each instance. The final optimization step is designed so that the decision is not made when the most informative fuzzy classifier does not exist.

Reconstructing the dynamic structure of a 3D scene is a fundamental challenge for robotic vision, which presents new challenges. In this work we present a new technique that involves a new, unified model based on spatial information, which can be used in a variety of applications. The spatial information is obtained by projecting the image from a 2D point to a 3D point using a low-level convolutional network. The 3D model automatically estimates the spatial information using the temporal analysis based on the temporal relationship of the image to the scene. In this paper we provide an extensive and thorough analysis for the spatial information in the 3D scene in terms of semantic relationships and joint visual features.

Video Anomaly Detection Using Learned Convnet Features

On the Complexity of Learning the Semantics of Verbal Morphology

An efficient framework for fuzzy classifiers

Selecting the Best Bases for Extractive Summarization

Learning to identify individual tumors from high resolution spectra via multi-scale principal component analysisReconstructing the dynamic structure of a 3D scene is a fundamental challenge for robotic vision, which presents new challenges. In this work we present a new technique that involves a new, unified model based on spatial information, which can be used in a variety of applications. The spatial information is obtained by projecting the image from a 2D point to a 3D point using a low-level convolutional network. The 3D model automatically estimates the spatial information using the temporal analysis based on the temporal relationship of the image to the scene. In this paper we provide an extensive and thorough analysis for the spatial information in the 3D scene in terms of semantic relationships and joint visual features.