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» On Kernel Methods for Relational Learning
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CORR
2010
Springer
109views Education» more  CORR 2010»
14 years 10 months ago
Edge Preserving Image Denoising in Reproducing Kernel Hilbert Spaces
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 ca...
Pantelis Bouboulis, Sergios Theodoridis
KDD
2009
ACM
229views Data Mining» more  KDD 2009»
16 years 3 months ago
Relational learning via latent social dimensions
Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather than classical IID distribution. To address the interdependency among data instances, ...
Lei Tang, Huan Liu
SIGPRO
2010
111views more  SIGPRO 2010»
14 years 10 months ago
Semi-supervised speaker identification under covariate shift
In this paper, we propose a novel semi-supervised speaker identification method that can alleviate the influence of non-stationarity such as session dependent variation, the recor...
Makoto Yamada, Masashi Sugiyama, Tomoko Matsui
ANNPR
2008
Springer
15 years 5 months ago
Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets
Clustering constitutes an ubiquitous problem when dealing with huge data sets for data compression, visualization, or preprocessing. Prototype-based neural methods such as neural g...
Alexander Hasenfuss, Barbara Hammer, Fabrice Rossi
KDD
2004
ACM
190views Data Mining» more  KDD 2004»
16 years 3 months ago
Kernel k-means: spectral clustering and normalized cuts
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis