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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...
BMCBI
2010
102views more  BMCBI 2010»
13 years 9 months ago
Peptide binding predictions for HLA DR, DP and DQ molecules
Background: MHC class II binding predictions are widely used to identify epitope candidates in infectious agents, allergens, cancer and autoantigens. The vast majority of predicti...
Peng Wang, John Sidney, Yohan Kim, Alessandro Sett...
BMCBI
2006
159views more  BMCBI 2006»
13 years 9 months ago
EVEREST: automatic identification and classification of protein domains in all protein sequences
Background: Proteins are comprised of one or several building blocks, known as domains. Such domains can be classified into families according to their evolutionary origin. Wherea...
Elon Portugaly, Amir Harel, Nathan Linial, Michal ...
ICML
2007
IEEE
14 years 10 months ago
Hierarchical Gaussian process latent variable models
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...
Neil D. Lawrence, Andrew J. Moore
ICML
2006
IEEE
14 years 10 months ago
Accelerated training of conditional random fields with stochastic gradient methods
We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several larg...
S. V. N. Vishwanathan, Nicol N. Schraudolph, Mark ...