Autonomous and deterministic probabilistic neural network using global k-means
We present a comparative study between Expectation-Maximization (EM) trained probabilistic neural network (PNN) with random initialization and with initialization from Global k-means. To make the results more comprehensive, the algorithm was tested on both homoscedastic and heteroscedastic PNNs. Nor...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
2006
|
| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/2066/ |
| _version_ | 1848789955162144768 |
|---|---|
| author | Chang, Roy Kwang Yang Loo, Chu Kiong, Chu Kiong Rao, , Machavaram V. C. |
| author_facet | Chang, Roy Kwang Yang Loo, Chu Kiong, Chu Kiong Rao, , Machavaram V. C. |
| author_sort | Chang, Roy Kwang Yang |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | We present a comparative study between Expectation-Maximization (EM) trained probabilistic neural network (PNN) with random initialization and with initialization from Global k-means. To make the results more comprehensive, the algorithm was tested on both homoscedastic and heteroscedastic PNNs. Normally, user have to define the number of clusters through trial and error method, which makes random initialization to be of stochastic nature. Global k-means was chosen as the initialization method because it can autonomously find the number of clusters using a selection criterion and can provide deterministic clustering results. The proposed algorithm was tested on benchmark datasets and real world data from the cooling water system in a power plant. |
| first_indexed | 2025-11-14T18:04:56Z |
| format | Article |
| id | mmu-2066 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:04:56Z |
| publishDate | 2006 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-20662011-08-10T07:04:46Z http://shdl.mmu.edu.my/2066/ Autonomous and deterministic probabilistic neural network using global k-means Chang, Roy Kwang Yang Loo, Chu Kiong, Chu Kiong Rao, , Machavaram V. C. QA75.5-76.95 Electronic computers. Computer science We present a comparative study between Expectation-Maximization (EM) trained probabilistic neural network (PNN) with random initialization and with initialization from Global k-means. To make the results more comprehensive, the algorithm was tested on both homoscedastic and heteroscedastic PNNs. Normally, user have to define the number of clusters through trial and error method, which makes random initialization to be of stochastic nature. Global k-means was chosen as the initialization method because it can autonomously find the number of clusters using a selection criterion and can provide deterministic clustering results. The proposed algorithm was tested on benchmark datasets and real world data from the cooling water system in a power plant. 2006 Article NonPeerReviewed Chang, Roy Kwang Yang and Loo, Chu Kiong, Chu Kiong and Rao, , Machavaram V. C. (2006) Autonomous and deterministic probabilistic neural network using global k-means. ADVANCES IN NEURAL NETWORKS - ISNN 2006, 3971 (1). pp. 830-836. ISSN 0302-9743 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Chang, Roy Kwang Yang Loo, Chu Kiong, Chu Kiong Rao, , Machavaram V. C. Autonomous and deterministic probabilistic neural network using global k-means |
| title | Autonomous and deterministic probabilistic neural network using global k-means |
| title_full | Autonomous and deterministic probabilistic neural network using global k-means |
| title_fullStr | Autonomous and deterministic probabilistic neural network using global k-means |
| title_full_unstemmed | Autonomous and deterministic probabilistic neural network using global k-means |
| title_short | Autonomous and deterministic probabilistic neural network using global k-means |
| title_sort | autonomous and deterministic probabilistic neural network using global k-means |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2066/ |