Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Computational Science & Engineering
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
A boosting approach for motif modeling using ChIP-chip data
Bioinformatics
Scale-Space Based Weak Regressors for Boosting
ECML '07 Proceedings of the 18th European conference on Machine Learning
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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Various forms of boosting techniques have been popularly used in many data mining and machine learning related applications. Inspite of their great success, boosting algorithms still suffer from a few open-ended problems that require closer investigation. The efficiency of any such ensemble technique significantly relies on the choice of the weak learners and the form of the loss function. In this paper, we propose a novel multi-resolution approach for choosing the weak learners during additive modeling. Our method applies insights from multi-resolution analysis and chooses the optimal learners at multiple resolutions during different iterations of the boosting algorithms. We demonstrate the advantages of using this novel framework for classification tasks and show results on different real-world datasets obtained from the UCI machine learning repository. Though demonstrated specifically in the context of boosting algorithms, our framework can be easily accommodated in general additive modeling techniques.