Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Bouboulis, Pantelis"'
We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parame
Externí odkaz:
http://arxiv.org/abs/1703.08131
We present a new framework for online Least Squares algorithms for nonlinear modeling in RKH spaces (RKHS). Instead of implicitly mapping the data to a RKHS (e.g., kernel trick), we map the data to a finite dimensional Euclidean space, using random f
Externí odkaz:
http://arxiv.org/abs/1606.03685
We consider the task of robust non-linear regression in the presence of both inlier noise and outliers. Assuming that the unknown non-linear function belongs to a Reproducing Kernel Hilbert Space (RKHS), our goal is to estimate the set of the associa
Externí odkaz:
http://arxiv.org/abs/1601.00595
The task of robust linear estimation in the presence of outliers is of particular importance in signal processing, statistics and machine learning. Although the problem has been stated a few decades ago and solved using classical (considered nowadays
Externí odkaz:
http://arxiv.org/abs/1409.4279
The paper presents a new framework for complex Support Vector Regression as well as Support Vector Machines for quaternary classification. The method exploits the notion of widely linear estimation to model the input-out relation for complex-valued d
Externí odkaz:
http://arxiv.org/abs/1303.2184
Recently, a unified framework for adaptive kernel based signal processing of complex data was presented by the authors, which, besides offering techniques to map the input data to complex Reproducing Kernel Hilbert Spaces, developed a suitable Wirtin
Externí odkaz:
http://arxiv.org/abs/1110.1075
Publikováno v:
Proceedings of the 20th International Conference on Pattern Recognition, Istanbul: Turkey, 23-26 August 2010
The goal of this paper is the development of a novel approach for the problem of Noise Removal, based on the theory of Reproducing Kernels Hilbert Spaces (RKHS). The problem is cast as an optimization task in a RKHS, by taking advantage of the celebr
Externí odkaz:
http://arxiv.org/abs/1011.5962
Over the last decade, kernel methods for nonlinear processing have successfully been used in the machine learning community. The primary mathematical tool employed in these methods is the notion of the Reproducing Kernel Hilbert Space. However, so fa
Externí odkaz:
http://arxiv.org/abs/1006.3033
Over the last decade, kernel methods for nonlinear processing have successfully been used in the machine learning community. However, so far, the emphasis has been on batch techniques. It is only recently, that online adaptive techniques have been co
Externí odkaz:
http://arxiv.org/abs/1005.0902
Although the real reproducing kernels are used in an increasing number of machine learning problems, complex kernels have not, yet, been used, in spite of their potential interest in applications such as communications. In this work, we focus our att
Externí odkaz:
http://arxiv.org/abs/1005.0897