Fast and Accurate Salient Object Segmentation – The detection and estimation of the motion of a human being by hand is a crucial task in many field environments and computer vision applications. In this paper, we propose three algorithms based on the principle of minimizing the sum factor and the sum of two terms for the optimal representation of human motion.
We present a new method for text mining that utilizes a combination of multiple semantic and syntactic distance measures to train an intelligent algorithm that is able to extract and recognize the semantic, syntactic and non-syntactic information from a corpus. We evaluate our approach using several datasets and compare the performance of the proposed method. We show that our method performs better than state-of-the-art word segmentation approaches, and that it achieves the best accuracy for recognizing semantic and syntactic information in a corpus.
We propose a novel novel non-negative matrix factorization algorithm based on sparse representation of a vector space. Our method outperforms the state-of-the-art in terms of solving the optimization problem by a significant margin. We present a comprehensive comparison between different approaches and demonstrate an improvement in the prediction performance for the supervised classification problem of MML.
The Effect of Size of Sample Enumeration on the Quality of Knowledge in Bayesian Optimization
Bayesian Online Nonparametric Adaptive Regression Models for Multivariate Time Series
Fast and Accurate Salient Object Segmentation
A Unified Approach for Scene Labeling Using Bilateral Filters
Toward Accurate Text Recognition via Transfer LearningWe present a new method for text mining that utilizes a combination of multiple semantic and syntactic distance measures to train an intelligent algorithm that is able to extract and recognize the semantic, syntactic and non-syntactic information from a corpus. We evaluate our approach using several datasets and compare the performance of the proposed method. We show that our method performs better than state-of-the-art word segmentation approaches, and that it achieves the best accuracy for recognizing semantic and syntactic information in a corpus.
We propose a novel novel non-negative matrix factorization algorithm based on sparse representation of a vector space. Our method outperforms the state-of-the-art in terms of solving the optimization problem by a significant margin. We present a comprehensive comparison between different approaches and demonstrate an improvement in the prediction performance for the supervised classification problem of MML.