.png){kind=icon}
Return
Kernel methods in machine learning
.png){kind=icon}
OA
AI
DOI：10.1214/009053607000000677.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. We cover a wide range of methods, ranging from binary classifiers to sophisticated methods for estimation with structured data.
Keywords:
machine learning
reproducing kernels
support vector machines
graphical models
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.7
Papers:
2.8K
Citations:
2.9W
Organization
Cited Papers
General exact solutions of Einstein equations for static perfect fluids with spherical symmetry
PREAI
ShareSaveThe reliability investigation of gold plated electrical contacts under sliding conditions.png)
Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)
Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)IF0
PREAI
ShareSaveShareSaveShareSaveShareSaveShareSave
View more