New Banknote Number Recognition Algorithm Based on Support Vector Machine

2013 ◽  
Author(s):  
Shan Gai ◽  
Guowei Yang ◽  
Sheng Zhang ◽  
Minghua Wan
Keyword(s):  
Support Vector Machine ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Number Recognition

scholarly journals Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 380 ◽  
Author(s):  
Kai Ye
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Recognition ◽  
False Positive Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Network Intrusion ◽  
Key Features

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.

An Automatic Target Recognition Algorithm Based on Support Vector Machine

2014 ◽  
Vol 496-500 ◽  
pp. 1873-1876
Author(s):  
He Zhang ◽  
Jie Li ◽  
Bei Bei Xu
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Support Vector Machine Model ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Machine Model ◽  
Fast Recognition ◽  
High False Alarm Rate

To improve the performance of automatic target recognition technology and solve the problems of traditional methods, such as high false alarm rate and poor adaptability to environment changes, a new algorithm based on support vector machine is proposed. We have realized the feature extraction of the target and the parameter optimization of the support vector machine to get the support vector machine model applied to the target recognition of unknown images. Experiment results show that the algorithm has a good recognition effect, a fast recognition speed and certain anti-interference abilities based on sufficient samples training.

Implementation of Classification and Recognition Algorithm for Text Information Based on Support Vector Machine

2019 ◽  
Vol 34 (08) ◽  
pp. 2053005
Author(s):  
Li Zhang
Keyword(s):  
Support Vector Machine ◽  
Automatic Classification ◽  
Recognition Algorithm ◽  
Public Security ◽  
Support Vector ◽  
Technology Support ◽  
Data Systems ◽  
Data Resource ◽  
Text Information ◽  
Security Information

At present, Internet data is the world’s largest data resource database. In order to realize fast and automatic intelligent classification, it is of great significance to develop automatic classification of public security intelligent data systems. This paper studies the actual needs of public security information text classification, analyzes the text automatic classification technology support vector machine (SVM) theory and designs and implements SVM-based public security information, and also realizes the classification system of public security information. Automatic classification provides support for subsequent text mining systems and text searches and designs the performance of the system. After optimization and testing, the system was found to have good practical application results.

A bark recognition algorithm for plant classification using a least square support vector machine

2016 ◽  
Author(s):  
Luis J. Blaanco ◽  
Carlos M. Travieso ◽  
Jose M. Quinteiro ◽  
Pablo V. Hernandez ◽  
Malay Kishore Dutta ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Recognition Algorithm ◽  
Least Square ◽  
Support Vector
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