A New SVM Multiclass Incremental Learning Algorithm

作者：Qin Yuping^*; Li Dan; Zhang Aihua

来源：Mathematical Problems in Engineering, 2015, 2015: 745815.

DOI：10.1155/2015/745815

A new support vector machine (SVM) multiclass incremental learning algorithm is proposed. To each class training sample, the hyperellipsoidal classifier that includes as many samples as possible and pushes the outlier samples away is trained in the feature space. When the new samples are added to the classification system, the algorithm reuses the old classifiers that have nothing to do with the new sample classes. To be classified sample, the Mahalanobis distances are used to decide the class of classified sample. If the sample point is not surrounded by any hyperellipsoidal or is surrounded by more than one hyperellipsoidal, the membership is used to confirm its class. The experimental results show that the algorithm has higher performance in classification precision and classification speed.

• 出版日期2015
• 单位渤海大学