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» Framework for Agile Methods Classification
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MICCAI
2002
Springer
14 years 8 months ago
Recognizing Deviations from Normalcy for Brain Tumor Segmentation
A framework is proposed for the segmentation of brain tumors from MRI. Instead of training on pathology, the proposed method trains exclusively on healthy tissue. The algorithm att...
David T. Gering, W. Eric L. Grimson, Ron Kikinis
ICML
2004
IEEE
14 years 8 months ago
The Bayesian backfitting relevance vector machine
Traditional non-parametric statistical learning techniques are often computationally attractive, but lack the same generalization and model selection abilities as state-of-the-art...
Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal
IJCV
2000
164views more  IJCV 2000»
13 years 7 months ago
Probabilistic Modeling and Recognition of 3-D Objects
This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical too...
Joachim Hornegger, Heinrich Niemann
ICDM
2010
IEEE
147views Data Mining» more  ICDM 2010»
13 years 5 months ago
Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach
Whenever a dataset has multiple discrete target variables, we want our algorithms to consider not only the variables themselves, but also the interdependencies between them. We pro...
Wouter Duivesteijn, Arno J. Knobbe, Ad Feelders, M...
TKDE
2011
479views more  TKDE 2011»
13 years 2 months ago
Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction
—Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis (LDA) is a traditional algorithm for supervised feat...
Wei Zhang, Zhouchen Lin, Xiaoou Tang