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» Data selection for support vector machine classifiers
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TOMS
1998
148views more  TOMS 1998»
14 years 9 months ago
PELLPACK: A Problem-Solving Environment for PDE-Based Applications on Multicomputer Platforms
This paper presents the software architecture and implementation of the problem solving environment (PSE) PELLPACK for modeling physical objects described by partial differential ...
Elias N. Houstis, John R. Rice, Sanjiva Weerawaran...
SSPR
2010
Springer
14 years 8 months ago
Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models
Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial d...
André F. T. Martins, Manuele Bicego, Vittor...
ICASSP
2011
IEEE
14 years 1 months ago
Online Kernel SVM for real-time fMRI brain state prediction
The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives stateof-the-art performance for brain state classification from funct...
Yongxin Taylor Xi, Hao Xu, Ray Lee, Peter J. Ramad...
SC
2009
ACM
15 years 4 months ago
Scalable temporal order analysis for large scale debugging
We present a scalable temporal order analysis technique that supports debugging of large scale applications by classifying MPI tasks based on their logical program execution order...
Dong H. Ahn, Bronis R. de Supinski, Ignacio Laguna...
BMCBI
2007
117views more  BMCBI 2007»
14 years 9 months ago
Meta-analysis of several gene lists for distinct types of cancer: A simple way to reveal common prognostic markers
Background: Although prognostic biomarkers specific for particular cancers have been discovered, microarray analysis of gene expression profiles, supported by integrative analysis...
Xinan Yang, Xiao Sun