Optimization-based Extreme Learning Machine with Multi-kernel Learning Approach for Classification

被引:11
作者
Cao, Le-le [1 ]
Huang, Wen-bing [1 ]
Sun, Fu-chun [1 ]
机构
[1] Tsinghua Univ, Dept Comp Sci & Technol, Tsinghua Natl Lab Informat Sci & Technol TNList, Beijing 100084, Peoples R China
来源
2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) | 2014年
关键词
multi-kernel extreme learning machine (MK-ELM); extreme learning machine (ELM); multi-kernel learning (MKL); optimization-based ELM; SimpleMKL; KERNEL; REGRESSION; NETWORKS;
D O I
10.1109/ICPR.2014.613
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The optimization method based extreme learning machine (optimization-based ELM) is generalized from single-hidden-layer feed-forward neural networks (SLFNs) by making use of kernels instead of neuron-alike hidden nodes. This approach is known for its high scalability, low computational complexity, and mild optimization constrains. The multi-kernel learning (MKL) framework SimpleMKL iteratively determines the combination of kernels by gradient descent wrapping a standard support vector machine (SVM) solver. SimpleMKL can be applied to many kinds of supervised learning problems to receive a more stable performance with rapid convergence speed. This paper proposes a new approach: MK-ELM (multi-kernel extreme learning machine) that applies SimpleMKL framework to the optimization-based ELM algorithm. The performance analysis on binary classification problems with various scales shows that MK-ELM tends to achieve the best generalization performance as well as being the most insensitive to parameters comparing to optimization-based ELM and SimpleMKL. As a result, MK-ELM can be implemented in real applications easily.
引用
收藏
页码:3564 / 3569
页数:6
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