Communications of the ACM
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
Prediction games and arcing algorithms
Neural Computation
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Machine Learning
Linear Programming Boosting via Column Generation
Machine Learning
Theoretical Views of Boosting and Applications
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
MadaBoost: A Modification of AdaBoost
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
An introduction to boosting and leveraging
Advanced lectures on machine learning
Robust Real-Time Face Detection
International Journal of Computer Vision
Column-generation boosting methods for mixture of kernels
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Boosting as a Regularized Path to a Maximum Margin Classifier
The Journal of Machine Learning Research
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
How boosting the margin can also boost classifier complexity
ICML '06 Proceedings of the 23rd international conference on Machine learning
Totally corrective boosting algorithms that maximize the margin
ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient Margin Maximizing with Boosting
The Journal of Machine Learning Research
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Boosting with structural sparsity
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Constructing ensembles of classifiers by means of weighted instance selection
IEEE Transactions on Neural Networks
Boosting through optimization of margin distributions
IEEE Transactions on Neural Networks
LACBoost and FisherBoost: optimally building cascade classifiers
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
On the Dual Formulation of Boosting Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble-based discriminant learning with boosting for face recognition
IEEE Transactions on Neural Networks
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We propose a general framework for analyzing and developing fully corrective boosting-based classifiers. The framework accepts any convex objective function, and allows any convex (for example, @?"p-norm, p=1) regularization term. By placing the wide variety of existing fully corrective boosting-based classifiers on a common footing, and considering the primal and dual problems together, the framework allows a direct comparison between apparently disparate methods. By solving the primal rather than the dual the framework is capable of generating efficient fully-corrective boosting algorithms without recourse to sophisticated convex optimization processes. We show that a range of additional boosting-based algorithms can be incorporated into the framework despite not being fully corrective. Finally, we provide an empirical analysis of the performance of a variety of the most significant boosting-based classifiers on a few machine learning benchmark datasets.