基于局部深度核学习的非线性支持向量机的有效预测
关键词 深度学习 局部多核学习 局部深度核学习 优化局部深度核学习
毕业论文设计说明书外文摘要
Title Efficient prediction of non-linear SVM based on local deep kernel learning
Abstract
This paper aims to speed up non-linear SVM prediction while maintaining classification accuracy above an acceptable limit. We generalize Localized Multiple Kernel Learning so as to learn a tree-based primal feature embedding which is high dimensional and sparse. This presents a significant challenge for non-linear SVMs since their cost of prediction can grow linearly with the size of the training set. Thus, even though non-linear SVMs has defined the state-of-the-art on multiple benchmark tasks, their use in real world applications remains limited. We develop a Local Deep Kernel Learning (LDKL) technique for efficient non-linear SVM prediction while maintaining classification accuracy above an acceptable threshold. Primal based classification decouples prediction costs from the number of support vectors and the size of the training set。Experiments on benchmark data sets reveal that LDKL can reduce prediction costs by more than three orders of magnitude in some cases with a moderate sacrifice in classification accuracy as compared to RBF-SVMs. Furthermore, LDKL can achieve better classification accuracies for speeding up non-linear SVM prediction. LDKL is significantly better than kernel approximation techniques, such as Random Fourier Features , as it focuses on the decision boundary rather than modeling the kernel everywhere in space. LDKL can also be much faster than reduced set methods as its tree-structured features can be computed in logarithmic time.
Keywords Deep Learning Localized Multiple Kernel Learning Local Deep Kernel Learning
目 录
1 引言 1
2 深度学习 3
2.1深度学习的介绍 3
2.2深度学习的基本思想 3
2.3深度学习的目标与体系 4
2.4深度学习的训练过程 5
3 深度核学习 7
3.1 相关工作 7