The Principle of Homology Continuity and Geometrical Covering Learning for Pattern Recognition

2018 ◽  
Vol 32 (12) ◽  
pp. 1850042 ◽  
Author(s):  
Xin Ning ◽  
Weijun Li ◽  
Jiang Xu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Fundamental Property ◽  
Experimental Results ◽  
New Method ◽  
Recognition Problem ◽  
Pattern Learning ◽  
Learning Method ◽  
Pattern Recognition Problem ◽  
Traditional Pattern

Homology Continuity is a fundamental property of the nature, but few of the traditional pattern recognition algorithms were aware of it. Firstly, this paper gives a brief description to the Principle of Homology Continuity (PHC), and tries to mathematically redefine it. Then, we introduce a PHC-based pattern learning method — Geometrical Covering Learning (GCL), following the Hyper sausage neural network as an instance of GCL. Lastly, we propose a GCL solution to the “two-spirals” pattern recognition problem. The final experimental results show that the new method is feasible and efficient.

scholarly journals The pattern recognition problem solution using a convolutional neural network

2020 ◽  
Vol 488 ◽  
pp. 012046
Author(s):  
K E Tokarev ◽  
A F Rogachev ◽  
Yu M Tokareva ◽  
A Yu Rudenko ◽  
D V Zelyakovskiy ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Recognition Problem ◽  
Problem Solution ◽  
Pattern Recognition Problem

scholarly journals Deep Hierarchical Representation from Classifying Logo-405

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Sujuan Hou ◽  
Jianwei Lin ◽  
Shangbo Zhou ◽  
Maoling Qin ◽  
Weikuan Jia ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Experimental Results ◽  
Fine Tuning ◽  
Related Research ◽  
Hierarchical Representation ◽  
Recognition Algorithms ◽  
Traditional Pattern

We introduce a logo classification mechanism which combines a series of deep representations obtained by fine-tuning convolutional neural network (CNN) architectures and traditional pattern recognition algorithms. In order to evaluate the proposed mechanism, we build a middle-scale logo dataset (named Logo-405) and treat it as a benchmark for logo related research. Our experiments are carried out on both the Logo-405 dataset and the publicly available FlickrLogos-32 dataset. The experimental results demonstrate that the proposed mechanism outperforms two popular ways used for logo classification, including the strategies that integrate hand-crafted features and traditional pattern recognition algorithms and the models which employ deep CNNs.

Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 2042-2045 ◽  
Author(s):  
Shang Jen Chuang ◽  
Chiung Hsing Chen ◽  
Chien Chih Kao ◽  
Fang Tsung Liu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gaussian Distribution ◽  
Test Pattern ◽  
Recognition Rate ◽  
Hopfield Neural Network ◽  
Training Data ◽  
New Method ◽  
Gaussian Filter ◽  
Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

Sign in / Sign up

Export Citation Format

Share Document