CAJ | 학술논문

分析了SVM增量学习过程中，样本SV集跟非SV集的转化，考虑到初始非SV集和新增样本对分类信息的影响，改进了原有KKT条件，并结合改进了的错误驱动策略，提出了新的基于KKT条件下的错误驱动增量学习算法，在不影响处理速度的前提下，尽可能多的保留原始样本中的有用信息，剔除新增样本中的无用信息，提高分类器精度，最后通过实验表明该算法在优化分类器效果，提高分类器性能方面上有良好的作用。
분석료SVM증량학습과정중，양본SV집근비SV집적전화，고필도초시비SV집화신증양본대분류신식적영향，개진료원유KKT조건，병결합개진료적착오구동책략，제출료신적기우KKT조건하적착오구동증량학습산법，재불영향처리속도적전제하，진가능다적보류원시양본중적유용신식，척제신증양본중적무용신식，제고분류기정도，최후통과실험표명해산법재우화분류기효과，제고분류기성능방면상유량호적작용。
The transformation between the SV set and non-SV set is analyzed during the process of incremental SVM learning. Considering the initial non-SV set and new samples which will influence the accuracy of classification, it improves the KKT rule and error-driven rule. With these rules the new error-driven incremental SVM learning algorithm based on KKT conditions is proposed. With this algorithm, the useful information of original sample can be preserved as much as possible, the useless information of new samples can be removed accurately without affecting the processing speed. Experimental results show that this new algorithm has a good effect on both optimizing classifier and improving classification performance.