.png){kind=icon}
Return
Support vector machines with applications
.png){kind=icon}
OA
AI
DOI：10.1214/088342306000000493.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Support vector machines (SVMs) appeared in the early nineties as optimal margin classifiers in the context of Vapnik's statistical learning theory. Since then SVMs have been successfully applied to real-world data analysis problems, often providing improved results compared with other techniques. The SVMs operate within the framework of regularization theory by minimizing an empirical risk in a well-posed and consistent way. A clear advantage of the support vector approach is that sparse solutions to classification and regression problems are usually obtained: only a few samples are involved in the determination of the classification or regression functions. This fact facilitates the application of SVMs to problems that involve a large amount of data, such as text processing and bioinformatics tasks. This paper is intended as an introduction to SVMs and their applications, emphasizing their key features. In addition, some algorithmic extensions and illustrative real-world applications of SVMs are shown.
Keywords:
support vector machines
kernel methods
regularization theory
classification
inverse problems
Journal
IF:
3.4
Papers:
1.0K
Citations:
8.7K
Organization
No organization information available
Cited Papers
ShareSaveElectrolytes for Li- and Na-Ion Batteries: Concepts, Candidates, and the Role of Nanotechnology
PREAI
ShareSaveShareSaveShareSaveShareSaveBayesian methods for support vector machines: Evidence and predictive class probabilities.png)
MACHINE LEARNING
OAAI
MACHINE LEARNINGIF2.9
OAAIShareSaveShareSave
View more