Zobrazeno 1 - 10
of 297
pro vyhledávání: '"Tran, Loc"'
Autor:
Tran, Loc Hoang
Face recognition is a very important topic in data science and biometric security research areas. It has multiple applications in military, finance, and retail, to name a few. In this paper, the novel hypergraph Laplacian Eigenmaps will be proposed a
Externí odkaz:
http://arxiv.org/abs/2405.16748
Autor:
Hung, Tran Loc
The Rotar central limit theorem is a remarkable theorem in the non-classical version since it does not use the condition of asymptotic infinitesimality for the independent individual summands, unlike the theorems named Lindeberg's and Lindeberg-Felle
Externí odkaz:
http://arxiv.org/abs/2309.13988
Autor:
Hung, Tran Loc
Since the appearance of H. Robbins article (1948), the central limit theorems for random sums have been studied for about 70 years. The central limit theorems for random sums of independent random variables play a very important role in various disci
Externí odkaz:
http://arxiv.org/abs/2307.16570
This paper constitutes the novel hypergraph convolutional neural networkbased clustering technique. This technique is employed to solve the clustering problem for the Citeseer dataset and the Cora dataset. Each dataset contains the feature matrix and
Externí odkaz:
http://arxiv.org/abs/2209.01391
This paper presents the novel way combining the BERT embedding method and the graph convolutional neural network. This combination is employed to solve the text classification problem. Initially, we apply the BERT embedding method to the texts (in th
Externí odkaz:
http://arxiv.org/abs/2111.15379
Face recognition is the very significant field in pattern recognition area. It has multiple applications in military and finance, to name a few. In this paper, the combination of the sparse PCA with the nearest-neighbor method (and with the kernel ri
Externí odkaz:
http://arxiv.org/abs/2112.00207
Publikováno v:
In Environmental Technology & Innovation May 2024 34
This paper presents a novel version of the hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in o
Externí odkaz:
http://arxiv.org/abs/2102.01934