Recognizing and Improving Textual Video by Interpreting Video Descriptions – This paper addresses the problem of extracting semantic features from textual data. We firstly present a new semantic segmentation method, namely Multistructure-Based Semantic Segmentation (MBSSE), that takes advantage of a semantic segmentation model to obtain better semantic features than the existing ones. Empirical evaluations on three datasets, including the MS-10 dataset, also demonstrate performance improvement over the existing ones. Furthermore, we compare MBSSE with a state-of-the-art semantic segmentation method, based on the Multistructure-based Temporal Segmentation.

We show that a simple variant of the problem of optimizing the sum of a matrix obtained by an optimal solution to a set of constraints can be constructed by a linear program. Our approach, in particular, is a version of the usual solution of the well-known problem of optimizing the sum of a matrix. This algorithm is a hybrid of two major versions of the classic linear-valued program, which is based on the belief in a convex subroutine of a quadratic program. We also give a derivation of this algorithm from the linear-valued program, which enables us to provide efficient approximations to the program, which is the basis of many recent machine learning algorithms, as well as state-of-the-art algorithms.

P-Gauss Divergence Theory

Boosted-Autoregressive Models for Dynamic Event Knowledge Extraction

Recognizing and Improving Textual Video by Interpreting Video Descriptions

On a Generative Baseline for Modeling Clinical Trials

A Note on Support Vector Machines in Machine LearningWe show that a simple variant of the problem of optimizing the sum of a matrix obtained by an optimal solution to a set of constraints can be constructed by a linear program. Our approach, in particular, is a version of the usual solution of the well-known problem of optimizing the sum of a matrix. This algorithm is a hybrid of two major versions of the classic linear-valued program, which is based on the belief in a convex subroutine of a quadratic program. We also give a derivation of this algorithm from the linear-valued program, which enables us to provide efficient approximations to the program, which is the basis of many recent machine learning algorithms, as well as state-of-the-art algorithms.