Benchmarking for Steganography
Information Hiding
Classification with Gaussians and Convex Loss
The Journal of Machine Learning Research
Optimal control of chaotic system based on LS-SVM with mixed kernel
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
The complex Gaussian kernel LMS algorithm
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
The use of hide in learning the value of a function
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
Covering numbers of Gaussian reproducing kernel Hilbert spaces
Journal of Complexity
On Dimension-independent Rates of Convergence for Function Approximation with Gaussian Kernels
SIAM Journal on Numerical Analysis
An explicit description of the extended gaussian kernel
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
Consistent identification of Wiener systems: A machine learning viewpoint
Automatica (Journal of IFAC)
Generalization Bounds of Regularization Algorithm with Gaussian Kernels
Neural Processing Letters
| Hi-index | 754.84 |
Although Gaussian radial basis function (RBF) kernels are one of the most often used kernels in modern machine learning methods such as support vector machines (SVMs), little is known about the structure of their reproducing kernel Hilbert spaces (RKHSs). In this work, two distinct explicit descriptions of the RKHSs corresponding to Gaussian RBF kernels are given and some consequences are discussed. Furthermore, an orthonormal basis for these spaces is presented. Finally, it is discussed how the results can be used for analyzing the learning performance of SVMs