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ICPR
2004
IEEE
16 years 3 months ago
Training of Classifiers Using Virtual Samples Only
This paper describes the training of classifiers entirely based on virtual images, rendered by a ray-tracing software. Two classifiers, a support vector machine and a polynomial c...
Annika Kuhl, Lars Krüger, Christian Wöhl...
TMI
2010
172views more  TMI 2010»
15 years 8 days 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
1998
IEEE
16 years 2 months ago
Feature Selection via Concave Minimization and Support Vector Machines
Computational comparison is made between two feature selection approaches for nding a separating plane that discriminates between two point sets in an n-dimensional feature space ...
Paul S. Bradley, Olvi L. Mangasarian
ICML
2004
IEEE
16 years 2 months ago
Robust feature induction for support vector machines
The goal of feature induction is to automatically create nonlinear combinations of existing features as additional input features to improve classification accuracy. Typically, no...
Rong Jin, Huan Liu
ICPR
2002
IEEE
16 years 3 months ago
Object Detection in Images: Run-Time Complexity and Parameter Selection of Support Vector Machines
In this paper we address two aspects related to the exploitation of Support Vector Machines (SVM) for classification in real application domains, such as the detection of objects ...
Nicola Ancona, Grazia Cicirelli, Ettore Stella, Ar...