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RSFDGRC
2005
Springer
187views Data Mining» more  RSFDGRC 2005»
15 years 10 months ago
Handling Missing Attribute Values in Preterm Birth Data Sets
The objective of our research was to find the best approach to handle missing attribute values in data sets describing preterm birth provided by the Duke University. Five strategi...
Jerzy W. Grzymala-Busse, Linda K. Goodwin, Witold ...
ESSMAC
2003
Springer
15 years 9 months ago
Filtered Gaussian Processes for Learning with Large Data-Sets
Kernel-based non-parametric models have been applied widely over recent years. However, the associated computational complexity imposes limitations on the applicability of those me...
Jian Qing Shi, Roderick Murray-Smith, D. M. Titter...
119
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ICCS
2003
Springer
15 years 9 months ago
A Method of Hidden Markov Model Optimization for Use with Geophysical Data Sets
Geophysics research has been faced with a growing need for automated techniques with which to process large quantities of data. A successful tool must meet a number of requirements...
Robert A. Granat
AI
2001
Springer
15 years 9 months ago
A Case Study for Learning from Imbalanced Data Sets
We present our experience in applying a rule induction technique to an extremely imbalanced pharmaceutical data set. We focus on using a variety of performance measures to evaluate...
Aijun An, Nick Cercone, Xiangji Huang
JMLR
2008
133views more  JMLR 2008»
15 years 4 months ago
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
Classifiers favoring sparse solutions, such as support vector machines, relevance vector machines, LASSO-regression based classifiers, etc., provide competitive methods for classi...
Suhrid Balakrishnan, David Madigan