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» A Minimax Method for Learning Functional Networks
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IJCNN
2006
IEEE
14 years 1 months ago
P-SVM Variable Selection for Discovering Dependencies Between Genetic and Brain Imaging Data
— The joint analysis of genetic and brain imaging data is the key to understand the genetic underpinnings of brain dysfunctions in several psychiatric diseases known to have a st...
Johannes Mohr, Imke Puis, Jana Wrase, Sepp Hochrei...
PAMI
2007
186views more  PAMI 2007»
13 years 7 months ago
Value-Directed Human Behavior Analysis from Video Using Partially Observable Markov Decision Processes
—This paper presents a method for learning decision theoretic models of human behaviors from video data. Our system learns relationships between the movements of a person, the co...
Jesse Hoey, James J. Little
KDD
2004
ACM
166views Data Mining» more  KDD 2004»
14 years 7 months ago
Predicting prostate cancer recurrence via maximizing the concordance index
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
Lian Yan, David Verbel, Olivier Saidi
IJCNN
2006
IEEE
14 years 1 months ago
Sparse Bayesian Models: Bankruptcy-Predictors of Choice?
Abstract— Making inferences and choosing appropriate responses based on incomplete, uncertainty and noisy data is challenging in financial settings particularly in bankruptcy de...
Bernardete Ribeiro, Armando Vieira, João Ca...
ICDAR
2011
IEEE
12 years 7 months ago
Subgraph Spotting through Explicit Graph Embedding: An Application to Content Spotting in Graphic Document Images
—We present a method for spotting a subgraph in a graph repository. Subgraph spotting is a very interesting research problem for various application domains where the use of a re...
Muhammad Muzzamil Luqman, Jean-Yves Ramel, Josep L...