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» Learning on the Test Data: Leveraging Unseen Features
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ICML
2000
IEEE
14 years 11 months ago
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
Most machine learning algorithms are lazy: they extract from the training set the minimum information needed to predict its labels. Unfortunately, this often leads to models that ...
Joseph O'Sullivan, John Langford, Rich Caruana, Av...
KDD
2001
ACM
216views Data Mining» more  KDD 2001»
14 years 10 months ago
The distributed boosting algorithm
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...
Aleksandar Lazarevic, Zoran Obradovic
CIKM
2009
Springer
14 years 4 months ago
L2 norm regularized feature kernel regression for graph data
Features in many real world applications such as Cheminformatics, Bioinformatics and Information Retrieval have complex internal structure. For example, frequent patterns mined fr...
Hongliang Fei, Jun Huan
ICTAI
1992
IEEE
14 years 2 months ago
Genetic Algorithms as a Tool for Feature Selection in Machine Learning
This paper describes an approach being explored to improve the usefulness of machine learning techniques for generating classification rules for complex, real world data. The appr...
Haleh Vafaie, Kenneth A. De Jong
JCIT
2010
190views more  JCIT 2010»
13 years 5 months ago
Application of Feature Extraction Method in Customer Churn Prediction Based on Random Forest and Transduction
With the development of telecom business, customer churn prediction becomes more and more important. An outstanding issue in customer churn prediction is high dimensional problem....
Yihui Qiu, Hong Li