Name
Abstract | ||
|---|---|---|
Kernel methods have become a standard solution for a large number of data analysis, and extensively utilized in the field of signal processing including analysis of speech, image, time series, and DNA sequences. The main difficulty in applying the kernel method is in designing the appropriate kernel for the specific data, and multiple kernel learning (MKL) is one of the principled approaches for kernel design problem. In this paper, a novel multiple kernel learning method based on the notion of Gaussianity evaluated by entropy power inequality is proposed. The notable characteristics of the proposed method are in utilizing the entropy power inequality for kernel learning, and in realizing an MKL algorithm which only optimizes the kernel combination coefficients, while the conventional methods need optimizing both combination coefficients and classifier parameters. The proposed MKL algorithm is experimentally shown to have good classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MLSP.2013.6661956 | MLSP |
Keywords | Field | DocType |
time series analysis,signal processing,discriminant analysis,gaussianity notion,learning (artificial intelligence),gaussian multiple kernel learning,pattern classification,data analysis,mkl,multiple kernel learning,speech analysis,combination coefficients,image analysis,entropy power inequality,gaussian processes,classification accuracy,classifier parameters,entropy,dna sequences analysis,learning artificial intelligence | Radial basis function kernel,Pattern recognition,Computer science,Kernel embedding of distributions,Multiple kernel learning,Kernel principal component analysis,Tree kernel,Artificial intelligence,String kernel,Variable kernel density estimation,Machine learning,Kernel (statistics) | Conference |
ISSN | Citations | PageRank |
1551-2541 | 1 | 0.35 |
References | Authors | |
9 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hideitsu Hino | 1 | 99 | 25.73 |