Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408133 | Neurocomputing | 2014 | 8 Pages |
Abstract
We introduce a fast sparse approximation schemes of extreme learning machine (ELM) named FSA-ELM of extreme learning machine (ELM). Our algorithms have two compelling features: low complexity and sparse solution. Experiments on benchmark data sets show that the proposed algorithm obtains sparse classifiers at a rather low complexity without sacrificing the generalization performance. As validated by the simulation results, FSA-ELM tends to have better scalability and achieves similar or much better generalization performance with much faster learning speed than the traditional ELM algorithm.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Xiaodong Li, Weijie Mao, Wei Jiang,