Name
Abstract | ||
|---|---|---|
This paper presents a comparative study of a group of methods based on Kernels which attempt to identify only the most significant cases with which to create the nonlinear Feature space. Kernels were originally derived in the context of Support Vector Machines which identify the smallest number of data points necessary to solve a particular problem (e.g. regression or classification). We use extensions of Kernel Principal Component Analysis to identify the optimal cases to create a sparse representation in Feature Space. The efficiency of the kernel models are compared on an oceanographic problem. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/KES.2000.885818 | KES'2000: FOURTH INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED INTELLIGENT ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, VOLS 1 AND 2, PROCEEDINGS |
Keywords | Field | DocType |
support vector machine,feature space,context modeling,regression,kernel principal component analysis,data points,intelligent systems,classification,kernel,computational intelligence,covariance matrix,principal component analysis,oceanography,support vector machines,efficiency,sparse matrices,sparse representation | Pattern recognition,Radial basis function kernel,Kernel embedding of distributions,Computer science,Kernel principal component analysis,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel method,String kernel,Variable kernel density estimation | Conference |
Citations | PageRank | References |
1 | 0.38 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Kun Gon | 1 | 1 | 0.38 |
| Junkang Feng | 2 | 13 | 12.49 |
| Colin Fyfe | 3 | 508 | 55.62 |