A New Model of Semantic Understanding for Video Summarization – In this work, we present a method for automatic word identification for Arabic and English. In this work, we first propose a novel algorithm for identifying a particular kind of word, that makes use of a vocabulary comprised of three terms as well as the nouns. Second, we propose a new method for identifying sentences whose relations are defined within a natural syntactic property called semantic relation: when the word is identified in the context of a given sentence, the semantic relation corresponds to the same word. Third, we analyze the problem to the extent that it arises in the context of a given sentence. In this framework, we formulate the problem as a two-part decision problem with the goal of selecting a word corresponding to a given sentence (and its related noun). Using some natural language processing tools, we show that the proposed system can recognize almost all sentences and the context of sentences corresponding to the given sentence in a sequence of experiments.

We present a new supervised learning framework for a novel problem: estimating the label space of natural images from a single, unlabeled unlabeled dataset of the same object within a given domain. While the supervised learning framework is widely applied to the recognition and labeling tasks, in this work we propose using a different classifier to automatically estimate the label space and find the right labels for the given domains. Experiments on the PASCAL VOC and CIFAR-10 datasets show that our framework provides significantly better results compared to the existing methods.

SQNet: Predicting the expected behavior of a target system using neural network

Learning-Based Matrix Factorization, t-SVD, and Bayesian Optimization

A New Model of Semantic Understanding for Video Summarization

An Analysis of A Simple Method for Clustering Sparsely

Learning Deep Transform Architectures using Label Class Discriminant AnalysisWe present a new supervised learning framework for a novel problem: estimating the label space of natural images from a single, unlabeled unlabeled dataset of the same object within a given domain. While the supervised learning framework is widely applied to the recognition and labeling tasks, in this work we propose using a different classifier to automatically estimate the label space and find the right labels for the given domains. Experiments on the PASCAL VOC and CIFAR-10 datasets show that our framework provides significantly better results compared to the existing methods.