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ICASSP
2010
IEEE
15 years 6 months ago
Parametric dictionary learning using steepest descent
In this paper, we suggest to use a steepest descent algorithm for learning a parametric dictionary in which the structure or atom functions are known in advance. The structure of ...
Mahdi Ataee, Hadi Zayyani, Massoud Babaie-Zadeh, C...
ECCC
2006
96views more  ECCC 2006»
15 years 6 months ago
When Does Greedy Learning of Relevant Features Succeed? --- A Fourier-based Characterization ---
Detecting the relevant attributes of an unknown target concept is an important and well studied problem in algorithmic learning. Simple greedy strategies have been proposed that s...
Jan Arpe, Rüdiger Reischuk
IJON
2006
99views more  IJON 2006»
15 years 6 months ago
Learning vector quantization: The dynamics of winner-takes-all algorithms
Winner-Takes-All (WTA) prescriptions for Learning Vector Quantization (LVQ) are studied in the framework of a model situation: Two competing prototype vectors are updated accordin...
Michael Biehl, Anarta Ghosh, Barbara Hammer
ECCV
2000
Springer
16 years 8 months ago
Unsupervised Learning of Models for Recognition
We present a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. We focus on a particular type of mode...
Markus Weber, Max Welling, Pietro Perona
ICARIS
2009
Springer
16 years 1 months ago
On AIRS and Clonal Selection for Machine Learning
AIRS is an immune-inspired supervised learning algorithm that has been shown to perform competitively on some common datasets. Previous analysis of the algorithm consists almost ex...
Chris McEwan, Emma Hart