Target recognition based on a dynamic SVM

A dynamic support vector machine method is applied to the target recognition using noise power spectrum. It searches the optimal separating hyperplane of the local space taking the target feature as center. To show better importance of each sample to the target feature, the penalty function is measu...

Full description

Saved in:
Bibliographic Details
Main Authors: Da Lianglong, Shi Guangzhi, Hu Junchuan, Pang Xiaonan
Format: Conference Proceeding
Language:English
Subjects:
Constraint optimization
Dynamic Support vector machine (DSVM)
Machine learning
Penalty function
Quadratic programming
Risk management
Sea measurements
Statistical learning
Support vector machine classification
Support vector machines
Target recognition
Underwater vehicles
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A dynamic support vector machine method is applied to the target recognition using noise power spectrum. It searches the optimal separating hyperplane of the local space taking the target feature as center. To show better importance of each sample to the target feature, the penalty function is measured by using the distance between the target feature and each training sample.
DOI:10.1109/MACE.2010.5535551