A Spatial Thresholding Method for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Strength of Weak Learnability
Machine Learning
Nonparametric resampling for homogeneous strong mixing random fields
Journal of Multivariate Analysis
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Bootstrapping to Assess and Improve Atmospheric Prediction Models
Data Mining and Knowledge Discovery
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
A clustering method based on boosting
Pattern Recognition Letters
Using boosting to prune bagging ensembles
Pattern Recognition Letters
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An experimental study of one- and two-level classifier fusion for different sample sizes
Pattern Recognition Letters
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We propose and assess a set of non-parametric ensembles, including bagging and boosting schemes, to recognize tumors in digital mammograms. Different approaches were examined as candidates for the two major components of the bagging ensembles, three spatial resampling schemes (residuals, centers and standardized centers), and four combination criteria (at least one, majority vote, top 25% models, and false discovery rate). A conversion to a classification problem prior to aggregation was employed for the boosting ensemble. The ensembles were compared at the lesion level against a single expert, and to a set of Markov Random Field (MRF) models in real images using three different criteria. The performance of the ensembles depended on its components, particularly the combination, with at least one and top 25% models offering a greater detection power independently of the type of lesion, and of the booststrapping scheme in a lesser degree. The ensembles were comparable in performance to MRFs in the unsupervised recognition of patterns exhibiting spatial structure.