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ML
2002
ACM
178views Machine Learning» more  ML 2002»
13 years 7 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
IPPS
1999
IEEE
14 years 7 days ago
Regularity Considerations in Instance-Based Locality Optimization
Instance based locality optimization 6 is a semi automatic program restructuring method that reduces the number of cache misses. The method imitates the human approach of consideri...
Claudia Leopold
MOC
2010
13 years 2 months ago
Portrait of the four regular super-exponentials to base sqrt(2)
We introduce the concept of regular super-functions at a fixed point. It is derived from the concept of regular iteration. A super-function F of h is a solution of F(z+1)=h(F(z)). ...
Dmitrii Kouznetsov, Henryk Trappmann
CORR
2008
Springer
79views Education» more  CORR 2008»
13 years 8 months ago
Free Distance Bounds for Protograph-Based Regular LDPC Convolutional Codes
In this paper asymptotic methods are used to form lower bounds on the free distance to constraint length ratio of several ensembles of regular, asymptotically good, protographbased...
David G. M. Mitchell, Ali Emre Pusane, Norbert Goe...
ICCV
2009
IEEE
15 years 28 days ago
Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers
We introduce a linearly weighted variant of the total variation for vector fields in order to formulate regularizers for multi-class labeling problems with non-trivial interclass...
J. Lellmann, F. Becker, C. Schn¨orr