CAJ | 학술논문

重点论述了基于MI图像特征选择方法~([1]),简要地讲述了支持向量机的SVMs分类器原理和设计~([2]).提出了MI贪婪最优算法,将高维数据处理转化为一维数据处理,简化了运算难度,同时提高了分类速度和准确性.实验结果表明,通过对8个分类、上千张图片进行分类处理,效果好于传统的分类算法.
중점논술료기우MI도상특정선택방법~([1]),간요지강술료지지향량궤적SVMs분류기원리화설계~([2]).제출료MI탐람최우산법,장고유수거처리전화위일유수거처리,간화료운산난도,동시제고료분류속도화준학성.실험결과표명,통과대8개분류、상천장도편진행분류처리,효과호우전통적분류산법.
This paper focuses on the MI-based image feature selection method~([1]),briefly describes SVMs category principle and design~([2]).The paper proposes the MI greedy optimal algorithm.transforms high-dimensional data processing into a one-dimensional data processing,simplifies the dificulty of the operation and increases the speed and accuracy of classification.The results show that,the eight classification,more than 1,000 classified images are better dealt with by the MI greedy optimal algorithm than the traditional classification algorithm.