CAJ | 학술논문

以支持向量机（ SVM）为代表的人工智能技术在智能传感器系统中得到了广泛的应用，但传统的SVM有“灾难性遗忘”现象，即会遗忘以前学过的知识，并且不能增量学习新的数据，这已无法满足智能传感器系统实时性的要求。而Learn＋＋算法能够增量地学习新来的数据，即使新来数据属于新的类，也不会遗忘已经学习到的旧知识。为了解决上述问题，提出了一种基于壳向量算法的Learn＋＋集成方法。实验结果表明：该算法不但具有增量学习的能力，而且在保证分类精度的同时，提高了训练速度，减小了存储规模，可以满足当下智能传感器系统在线学习的需求。
이지지향량궤（ SVM）위대표적인공지능기술재지능전감기계통중득도료엄범적응용，단전통적SVM유“재난성유망”현상，즉회유망이전학과적지식，병차불능증량학습신적수거，저이무법만족지능전감기계통실시성적요구。이Learn＋＋산법능구증량지학습신래적수거，즉사신래수거속우신적류，야불회유망이경학습도적구지식。위료해결상술문제，제출료일충기우각향량산법적Learn＋＋집성방법。실험결과표명：해산법불단구유증량학습적능력，이차재보증분류정도적동시，제고료훈련속도，감소료존저규모，가이만족당하지능전감기계통재선학습적수구。
Support vector machine( SVM)as the representative of artificial intelligent techniques has been widely used in the intelligent sensor system,however,traditional SVM suffers from the catastrophic forgetting phenomenon,which results in loss of previously learned information,so it is unable to meet the requirements of real-time intelligent sensor system. The strength of Learn++ lies in its ability to learn new data without forgetting previously acquired knowledge,even when the new data introduce new classes. In order to solve the above problem,a Learn++ integration method based on hull vectors is proposed. Experimental results show that the algorithm not only has the ability of incremental learning,improve training speed and reduce the storage size,but also can ensure the classification precision,which meets the current demand of intelligent sensor systems for online learning.