CAJ | 학술논문

提出一种基于马氏超椭球学习机的多类文本分类算法。对每一类训练样本，训练马氏超椭球学习机，使其包含该类尽可能多的样本，同时将噪音点排除在外。对于待分类样本，通过待分类样本的映射到每个超椭球球心的马氏距离确定其类别。实验结果表明，该算法提高了分类精度和分类速度。
제출일충기우마씨초타구학습궤적다류문본분류산법。대매일류훈련양본，훈련마씨초타구학습궤，사기포함해류진가능다적양본，동시장조음점배제재외。대우대분류양본，통과대분류양본적영사도매개초타구구심적마씨거리학정기유별。실험결과표명，해산법제고료분류정도화분류속도。
A new multiclass text classification algorithm based on mahalanobis hyper ellipsoidal learning ma -chine is proposed .To each class sample , training the hyper ellipsoidal learning machine , which include as much the class samples as possible and push the outlier samples away .For the sample to be classified , the distance from the sample mapping to the center of each mahalanobis hyper ellipsoidal are used to decide the sample classs.The results of the experiment show that the proposed algorithm has a higher classification accuracy and classification speed .