Name
Abstract | ||
|---|---|---|
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machines. Unfortunately, after learning, the computational complexity of execution through a kernel is of the order of the size of the training set, which is quite large for many applications. This paper proposes a two-step procedure for arriving at a compact and computationally efficient execution procedure. After learning in the kernel space, the proposed extension exploits the universal approximation capabilities of generalized radial basis function neural networks to efficiently approximate and replace the projections onto the empirical kernel map used during execution. Sample applications demonstrate significant compression of the kernel representation with graceful performance loss. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TNN.2011.2127485 | IEEE Transactions on Neural Networks |
Keywords | DocType | Volume |
kernel representation,computationally efficient execution procedure,two-step procedure,empirical kernel,kernel map compression,statistical learning theory,mercer kernel method,computational complexity,kernel space,kernel-based methods,strong learning capability,kernel principal component analysis,artificial intelligence,artificial neural network,optimization,artificial neural networks,kernel methods,support vector machines,data compression,algorithms,learning artificial intelligence,support vector machine,radial basis functions,machine learning,clustering algorithms,principal component analysis,radial basis function,computer simulation,kernel method,kernel | Journal | 22 |
Issue | ISSN | Citations |
6 | 1941-0093 | 2 |
PageRank | References | Authors |
0.38 | 20 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Omar Arif | 1 | 22 | 5.87 |
| Patricio A Vela | 2 | 46 | 2.91 |