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» Mining Several Data Bases with an Ensemble of Classifiers
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ML
2010
ACM
138views Machine Learning» more  ML 2010»
13 years 2 months ago
Mining adversarial patterns via regularized loss minimization
Traditional classification methods assume that the training and the test data arise from the same underlying distribution. However, in several adversarial settings, the test set is...
Wei Liu, Sanjay Chawla
KDD
2009
ACM
150views Data Mining» more  KDD 2009»
14 years 8 months ago
Information theoretic regularization for semi-supervised boosting
We present novel semi-supervised boosting algorithms that incrementally build linear combinations of weak classifiers through generic functional gradient descent using both labele...
Lei Zheng, Shaojun Wang, Yan Liu, Chi-Hoon Lee
EUROGP
2010
Springer
166views Optimization» more  EUROGP 2010»
14 years 24 days ago
Learning a Lot from Only a Little: Genetic Programming for Panel Segmentation on Sparse Sensory Evaluation Data
We describe a data mining framework that derives panelist information from sparse flavour survey data. One component of the framework executes genetic programming ensemble based s...
Katya Vladislavleva, Kalyan Veeramachaneni, Una-Ma...
SEKE
2005
Springer
14 years 1 months ago
From Data to Knowledge: an Integrated Rule-Based Data Mining System
This paper presents an integrated rule-based data mining system that is capable of creating rulebased classifiers with web-based user interface from data sets provided by end user...
Chien-Chung Chan, Zhicheng Su
ICONIP
2008
13 years 9 months ago
The Diversity of Regression Ensembles Combining Bagging and Random Subspace Method
Abstract. The concept of Ensemble Learning has been shown to increase predictive power over single base learners. Given the bias-variancecovariance decomposition, diversity is char...
Alexandra Scherbart, Tim W. Nattkemper