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PRL
2011
15 years 1 months ago
Consistency of functional learning methods based on derivatives
In some real world applications, such as spectrometry, functional models achieve better predictive performances if they work on the derivatives of order m of their inputs rather t...
Fabrice Rossi, Nathalie Villa-Vialaneix
ALT
2001
Springer
16 years 3 months ago
Learning of Boolean Functions Using Support Vector Machines
This paper concerns the design of a Support Vector Machine (SVM) appropriate for the learning of Boolean functions. This is motivated by the need of a more sophisticated algorithm ...
Ken Sadohara
NIPS
2003
15 years 7 months ago
Gene Expression Clustering with Functional Mixture Models
We propose a functional mixture model for simultaneous clustering and alignment of sets of curves measured on a discrete time grid. The model is specifically tailored to gene exp...
Darya Chudova, Christopher E. Hart, Eric Mjolsness...
SIAMSC
2008
237views more  SIAMSC 2008»
15 years 6 months ago
A Variational Shape Optimization Approach for Image Segmentation with a Mumford--Shah Functional
We introduce a novel computational method for a Mumford-Shah functional, which decomposes a given image into smooth regions separated by closed curves. Casting this as a shape opti...
Günay Dogan, Pedro Morin, Ricardo H. Nochetto
DAM
1998
65views more  DAM 1998»
15 years 5 months ago
Minimization of an M-convex Function
M-convex functions, introduced by Murota (1996, 1998), enjoy various desirable properties as “discrete convex functions.” In this paper, we propose two new polynomial-time sca...
Akiyoshi Shioura