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110
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ICML
2007
IEEE
16 years 1 months ago
Dimensionality reduction and generalization
In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning ...
Sofia Mosci, Lorenzo Rosasco, Alessandro Verri
JMLR
2006
186views more  JMLR 2006»
15 years 18 days ago
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
ISVLSI
2006
IEEE
104views VLSI» more  ISVLSI 2006»
15 years 6 months ago
Adaptive Signal Processing in Mixed-Signal VLSI with Anti-Hebbian Learning
We describe analog and mixed-signal primitives for implementing adaptive signal-processing algorithms in VLSI based on anti-Hebbian learning. Both on-chip calibration techniques a...
Miguel Figueroa, Esteban Matamala, Gonzalo Carvaja...
TSP
2011
152views more  TSP 2011»
14 years 7 months ago
Blind Adaptive Constrained Constant-Modulus Reduced-Rank Interference Suppression Algorithms Based on Interpolation and Switched
—This work proposes a blind adaptive reduced-rank scheme and constrained constant-modulus (CCM) adaptive algorithms for interference suppression in wireless communications system...
Rodrigo C. de Lamare, Raimundo Sampaio Neto, Marti...
113
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SIMPRA
2008
125views more  SIMPRA 2008»
15 years 17 days ago
Identification of Wiener models using optimal local linear models
The Wiener model is a versatile nonlinear block oriented model structure for miscellaneous applications. In this paper a method for identifying the parameters of such a model usin...
Martin Kozek, Sabina Sinanovic