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TMI
2010
172views more  TMI 2010»
13 years 7 months ago
Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease Through Automated Hippocampal Segmentation
Abstract— We compared four automated methods for hippocampal segmentation using different machine learning algorithms (1) hierarchical AdaBoost, (2) Support Vector Machines (SVM)...
Jonathan H. Morra, Zhuowen Tu, Liana G. Apostolova...
ICML
2006
IEEE
14 years 9 months ago
Simpler knowledge-based support vector machines
If appropriately used, prior knowledge can significantly improve the predictive accuracy of learning algorithms or reduce the amount of training data needed. In this paper we intr...
Quoc V. Le, Alex J. Smola, Thomas Gärtner
ICML
2006
IEEE
14 years 9 months ago
The support vector decomposition machine
In machine learning problems with tens of thousands of features and only dozens or hundreds of independent training examples, dimensionality reduction is essential for good learni...
Francisco Pereira, Geoffrey J. Gordon
IJCNN
2006
IEEE
14 years 2 months ago
A Heuristic for Free Parameter Optimization with Support Vector Machines
— A heuristic is proposed to address free parameter selection for Support Vector Machines, with the goals of improving generalization performance and providing greater insensitiv...
Matthew Boardman, Thomas P. Trappenberg
TMI
2002
143views more  TMI 2002»
13 years 8 months ago
A Support Vector Machine Approach for Detection of Microcalcifications
In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive ...
Issam El-Naqa, Yongyi Yang, Miles N. Wernick, Niko...