This paper considers a robust kernel regularized classification algorithm with a non-convex loss function which is proposed to alleviate the performance deterioration caused by the outliers.A comparison relationship b...This paper considers a robust kernel regularized classification algorithm with a non-convex loss function which is proposed to alleviate the performance deterioration caused by the outliers.A comparison relationship between the excess misclassification error and the excess generalization error is provided;from this,along with the convex analysis theory,a kind of learning rate is derived.The results show that the performance of the classifier is effected by the outliers,and the extent of impact can be controlled by choosing the homotopy parameters properly.展开更多
鉴于训练和测试阶段存在不同的噪声或混响环境,并且由于真实数据的稀缺会降低语音声源到达方向(Direction of Arrival, DOA)的分类准确性,因此提出一种基于核函数领域自适应的机器学习DOA分类算法。通过优化结构风险函数和减小域之间的...鉴于训练和测试阶段存在不同的噪声或混响环境,并且由于真实数据的稀缺会降低语音声源到达方向(Direction of Arrival, DOA)的分类准确性,因此提出一种基于核函数领域自适应的机器学习DOA分类算法。通过优化结构风险函数和减小域之间的条件分布差异,实现对训练数据的适应性学习,从而提升测试数据的分类准确率。实验结果证明在中小型数据集中,新算法在各种声学条件下均明显优于对比的深度学习算法。展开更多
基金supported by the NSF(61877039)the NSFC/RGC Joint Research Scheme(12061160462 and N City U 102/20)of China+2 种基金the NSF(LY19F020013)of Zhejiang Provincethe Special Project for Scientific and Technological Cooperation(20212BDH80021)of Jiangxi Provincethe Science and Technology Project in Jiangxi Province Department of Education(GJJ211334)。
文摘This paper considers a robust kernel regularized classification algorithm with a non-convex loss function which is proposed to alleviate the performance deterioration caused by the outliers.A comparison relationship between the excess misclassification error and the excess generalization error is provided;from this,along with the convex analysis theory,a kind of learning rate is derived.The results show that the performance of the classifier is effected by the outliers,and the extent of impact can be controlled by choosing the homotopy parameters properly.
文摘鉴于训练和测试阶段存在不同的噪声或混响环境,并且由于真实数据的稀缺会降低语音声源到达方向(Direction of Arrival, DOA)的分类准确性,因此提出一种基于核函数领域自适应的机器学习DOA分类算法。通过优化结构风险函数和减小域之间的条件分布差异,实现对训练数据的适应性学习,从而提升测试数据的分类准确率。实验结果证明在中小型数据集中,新算法在各种声学条件下均明显优于对比的深度学习算法。
