CAJ | 학술논문

提出一种基于Madaline网络敏感性的主动学习算法。首先用部分样本训练Madaline网络，然后以Madaline网络输出对其输入在给定样本点附近变化的敏感性为尺度，主动从未参与训练的样本中挑选敏感性相对大的样本继续进行训练，循环反复这个过程直到满足训练要求为止。实验验证了该主动学习算法处理离散分类问题的有效性和可行性。
제출일충기우Madaline망락민감성적주동학습산법。수선용부분양본훈련Madaline망락，연후이Madaline망락수출대기수입재급정양본점부근변화적민감성위척도，주동종미삼여훈련적양본중도선민감성상대대적양본계속진행훈련，순배반복저개과정직도만족훈련요구위지。실험험증료해주동학습산법처리리산분류문제적유효성화가행성。
This paper presents an active learning algorithm based on the Madaline network's output sensitivity due to its input variation near a given sample. First, a portion of samples are used to train a Madaline network. Then, based on the sensitivity of the network, some other samples with higher sensitivity are actively selected and added into the training data to continue the training of the network. This process is repeated until the training requirement is met. Experiments verified the effectiveness and feasibility of the proposed active learning algorithm in treating discrete classification problems.