CAJ | 학술논문

基于算法随机性理论提出的直推式置信机器能够给出预测的可靠性，但其多用于解决两类识别问题。扩展了置信机器，利用了正反类的思想，在识别时比较多个P值来确定测试样本的分类，使其很容易一次性应用于多分类识别问题。为对扩展后的模型性能进行评估，将其应用于经典的模式识别-人脸识别。实验结果表明，扩展后的置信机器具有良好的分类性能，当每类训练集样本增加到6个时，识别率已高于96%。
기우산법수궤성이론제출적직추식치신궤기능구급출예측적가고성，단기다용우해결량류식별문제。확전료치신궤기，이용료정반류적사상，재식별시비교다개P치래학정측시양본적분류，사기흔용역일차성응용우다분류식별문제。위대확전후적모형성능진행평고，장기응용우경전적모식식별-인검식별。실험결과표명，확전후적치신궤기구유량호적분류성능，당매류훈련집양본증가도6개시，식별솔이고우96%。
Transductive confidence machine is method based on random algorithm theory. It can estimate the reliability of a prediction but has mainly been applied binary classification problems. This paper extends the transductive confidence machine using the idea of positive and negative classes. The extended Transductive Confidence Machine(TCM)does classification by comparing multiple P values and can be applied to multi-class problems. The new algorithm is applied to face recognition and achieves a recognition rate of 96%even when each class contains only 6 training samples.