Name
Abstract | ||
|---|---|---|
The Bayesian ARTMAP neural network, introduced by Vigdor and Lerner, is an incremental learning algorithm which can efficiently process massive datasets for classification, regression, and probabilistic inference tasks. We introduce the parallelized version of the BA neural network and implement it in OpenCL. Our implementation runs on both multi-core CPUs and GPUs architectures. We test the Parallel Bayesian ARTMAP on several classification and regression benchmarks focusing on speedup and scalability. In some cases, the parallel BA runs by an order of magnitude faster than the sequential implementation. Our implementation has the potential to scale for OpenCL devices with increasing number of compute units. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s11063-017-9663-x | Neural Processing Letters |
Keywords | Field | DocType |
Bayesian learning,Incremental learning,Fuzzy ARTMAP,OpenCL,Parallel architectures,Massive datasets | Probabilistic inference,Bayesian inference,Regression,Computer science,Parallel computing,Artificial intelligence,Artificial neural network,Incremental learning algorithm,Machine learning,Speedup,Bayesian probability,Scalability | Journal |
Volume | Issue | ISSN |
47 | 2 | 1370-4621 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| István Lorentz | 1 | 4 | 1.14 |
| Razvan Andonie | 2 | 117 | 17.71 |
| Lucian Sasu | 3 | 27 | 3.89 |