Name
Abstract | ||
|---|---|---|
The success of kernel methods is very much dependent on the choice of kernel. Kernel design and learning a kernel from the data require evaluation measures to assess the quality of the kernel. In recent years, the notion of kernel alignment, which measures the degree of agreement between a kernel and a learning task, is widely used for kernel selection due to its effectiveness and low computational complexity. In this paper, we present an overview of the research progress of kernel alignment and its applications. We introduce the basic idea of kernel alignment and its theoretical properties, as well as the extensions and improvements for specific learning problems. The typical applications, including kernel parameter tuning, multiple kernel learning, spectral kernel learning and feature selection and extraction, are reviewed in the context of classification framework. The relationship between kernel alignment and other evaluation measures is also explored. Finally, concluding remarks and future directions are presented. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s10462-012-9369-4 | Artif. Intell. Rev. |
Keywords | Field | DocType |
Kernel alignment,Kernel evaluation measure,Learning kernels,Kernel method,Model selection | Graph kernel,Radial basis function kernel,Kernel embedding of distributions,Computer science,Multiple kernel learning,Geometric modeling kernel,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel method,Machine learning | Journal |
Volume | Issue | ISSN |
43 | 2 | 0269-2821 |
Citations | PageRank | References |
16 | 0.57 | 37 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tinghua Wang | 1 | 44 | 2.42 |
| Dongyan Zhao | 2 | 998 | 96.35 |
| Shengfeng Tian | 3 | 312 | 22.64 |