A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Scale-Space Theory
Scale-Space Kernels for Additive Modeling
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Multi-resolution Boosting for Classification and Regression Problems
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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Boosting is a simple yet powerful modeling technique that is used in many machine learning and data mining related applications. In this paper, we propose a novel scale-space based boosting framework which applies scale-space theory for choosing the optimal regressors during the various iterations of the boosting algorithm. In other words, the data is considered at different resolutions for each iteration in the boosting algorithm. Our framework chooses the weak regressors for the boosting algorithm that can best fit the current resolution and as the iterations progress, the resolution of the data is increased. The amount of increase in the resolution follows from the wavelet decomposition methods. For regression modeling, we use logitboost update equations based on first derivative of the loss function. We clearly manifest the advantages of using this scale-space based framework for regression problems and show results on different real-world regression datasets.