Kernel Methods in Machine Learning
2008
Article
ei
We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data.
Author(s): | Hofmann, T. and Schölkopf, B. and Smola, AJ. |
Journal: | Annals of Statistics |
Volume: | 36 |
Number (issue): | 3 |
Pages: | 1171-1220 |
Year: | 2008 |
Month: | June |
Day: | 0 |
Department(s): | Empirical Inference |
Bibtex Type: | Article (article) |
Digital: | 0 |
DOI: | 10.1214/009053607000000677 |
Institution: | Max Planck Institute for Biological Cybernetics, Tübingen |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
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BibTex @article{4268, title = {Kernel Methods in Machine Learning}, author = {Hofmann, T. and Sch{\"o}lkopf, B. and Smola, AJ.}, journal = {Annals of Statistics}, volume = {36}, number = {3}, pages = {1171-1220}, organization = {Max-Planck-Gesellschaft}, institution = {Max Planck Institute for Biological Cybernetics, Tübingen}, school = {Biologische Kybernetik}, month = jun, year = {2008}, doi = {10.1214/009053607000000677}, month_numeric = {6} } |