FOCM
2008
13 years 11 months ago
2008
This paper considers the least-square online gradient descent algorithm in a reproducing kernel Hilbert space (RKHS) without explicit regularization. We present a novel capacity i...
ADCM
2006
13 years 11 months ago
2006
This paper investigates the approximation of multivariate functions from data via linear combinations of translates of a positive definite kernel from a reproducing kernel Hilbert...
CORR
2008
Springer
13 years 11 months ago
2008
Springer
We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two sample...
AUTOMATICA
2010
13 years 11 months ago
2010
This paper describes a new kernel-based approach for linear system identification of stable systems. We model the impulse response as the realization of a Gaussian process whose s...
ICASSP
2010
IEEE
13 years 11 months ago
2010
IEEE
The sequence kernel has been shown to be a promising kernel function for learning from sequential data such as speech and DNA. However, it is not scalable to massive datasets due ...
NIPS
2003
14 years 27 days ago
2003
We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solu...
NIPS
2004
14 years 27 days ago
2004
We formulate the problem of graph inference where part of the graph is known as a supervised learning problem, and propose an algorithm to solve it. The method involves the learni...
NIPS
2004
14 years 27 days ago
2004
We describe methods for computing an implicit model of a hypersurface that is given only by a finite sampling. The methods work by mapping the sample points into a reproducing ker...
ESANN
2003
14 years 27 days ago
2003
Abstract. We focus on covariance criteria for finding a suitable subspace for regression in a reproducing kernel Hilbert space: kernel principal component analysis, kernel partial...
NIPS
2008
14 years 29 days ago
2008
In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation scheme we propose the use o...