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ML
2007
ACM
144views Machine Learning» more  ML 2007»
13 years 7 months ago
Invariant kernel functions for pattern analysis and machine learning
In many learning problems prior knowledge about pattern variations can be formalized and beneficially incorporated into the analysis system. The corresponding notion of invarianc...
Bernard Haasdonk, Hans Burkhardt
COLT
2008
Springer
13 years 9 months ago
Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning
We study the potential benefits to classification prediction that arise from having access to unlabeled samples. We compare learning in the semi-supervised model to the standard, ...
Shai Ben-David, Tyler Lu, Dávid Pál
TNN
1998
111views more  TNN 1998»
13 years 7 months ago
Asymptotic distributions associated to Oja's learning equation for neural networks
— In this paper, we perform a complete asymptotic performance analysis of the stochastic approximation algorithm (denoted subspace network learning algorithm) derived from Oja’...
Jean Pierre Delmas, Jean-Francois Cardos
ENVSOFT
2000
86views more  ENVSOFT 2000»
13 years 7 months ago
Broken line smoothing: a simple method for interpolating and smoothing data series
A technique is proposed for smoothing a broken line fit, with known break points, to observational data. It will be referred to as "broken line smoothing". The smoothness...
Demetris Koutsoyiannis
ICPR
2010
IEEE
13 years 9 months ago
SemiCCA: Efficient Semi-Supervised Learning of Canonical Correlations
Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limit...
Akisato Kimura, Hirokazu Kameoka, Masashi Sugiyama...