Original Contribution: Stacked generalization
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Ensemble Learning for Data Mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Visual Vocabulary for Flower Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Automated Flower Classification over a Large Number of Classes
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
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Over the last few years, several approaches have been proposed for information fusion including different variants of classifier level fusion (ensemble methods), stacking and multiple kernel learning (MKL). MKL has become a preferred choice for information fusion in object recognition. However, in the case of highly discriminative and complementary feature channels, it does not significantly improve upon its trivial baseline which averages the kernels. Alternative ways are stacking and classifier level fusion (CLF) which rely on a two phase approach. There is a significant amount of work on linear programming formulations of ensemble methods particularly in the case of binary classification. In this paper we propose a multiclass extension of binary v-LPBoost, which learns the contribution of each class in each feature channel. The existing approaches of classifier fusion promote sparse features combinations, due to regularization based on l1-norm, and lead to a selection of a subset of feature channels, which is not good in the case of informative channels. Therefore, we generalize existing classifier fusion formulations to arbitrary lp-norm for binary and multiclass problems which results in more effective use of complementary information. We also extended stacking for both binary and multiclass datasets. We present an extensive evaluation of the fusion methods on four datasets involving kernels that are all informative and achieve state-of-the-art results on all of them.