Name
Abstract | ||
|---|---|---|
This paper presents a two-stage learning algorithm to reduce the hidden nodes of a radial basis function network (RBFN). The first stage involves the construction of an RBFN using the dynamic decay adjustment (DDA) and the second stage involves the use of a modified histogram algorithm (HIST) to reduce hidden neurons. DDA enables the RBFN to perform constructive learning without pre-defining the number of hidden nodes. The learning process of DDA is fast but it tends to generate a large network architecture as a result of its greedy insertion behavior. Therefore, an RBFNDDA-HIST is proposed to reduce the nodes. The proposed RBFNDDA-HIST is tested with three benchmark medical datasets. The experimental results show that the accuracy of the RBFNDDA-HIST is compatible with to that of RBFNDDA but with less number of nodes. This proposed network is favorable in a real environment because the computation cost can be reduced. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-12637-1_16 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
radial basis network,nodes reduction,histogram,dynamic decay adjustment | Histogram,Radial basis network,Radial basis function network,Constructive learning,Pattern recognition,Computer science,Network architecture,Algorithm,Artificial intelligence,Reduction procedure,Machine learning,Computation | Conference |
Volume | ISSN | Citations |
8834 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pey Yun Goh | 1 | 1 | 1.36 |
| Shing Chiang Tan | 2 | 122 | 18.99 |
| Wooi Ping Cheah | 3 | 36 | 8.03 |