Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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
Multiclass Boosting for Weak Classifiers
The Journal of Machine Learning Research
Multiclass boosting with repartitioning
ICML '06 Proceedings of the 23rd international conference on Machine learning
A detector tree of boosted classifiers for real-time object detection and tracking
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Boosting for Learning Multiple Classes with Imbalanced Class Distribution
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Unifying multi-class AdaBoost algorithms with binary base learners under the margin framework
Pattern Recognition Letters
Multi-Class Learning by Smoothed Boosting
Machine Learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
High-Performance Rotation Invariant Multiview Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
Coarse-To-Fine Multiclass Nested Cascades for Object Detection
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A new ensemble-based cascaded framework for multiclass training with simple weak learners
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Ensemble Machine Learning: Methods and Applications
Ensemble Machine Learning: Methods and Applications
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This paper presents a coarse-to-fine learning algorithm for multiclass problems. The algorithm is applied to ensemble-based learning by using boosting to construct cascades of classifiers. The goal is to address the training and detection runtime complexities found in an increasing number of classification domains. This research applies a separate-and-conquer strategy with respect to class labels, in order to realize efficiency in both the training and detection phases under limited computational resources, without compromising accuracy. The paper demonstrates how popular, non-cascaded algorithms like AdaBoost.M2, AdaBoost.OC and AdaBoost.ECC can be converted into robust cascaded classifiers. Additionally, a new multiclass weak learner is proposed that is custom designed for cascaded training. Experiments were conducted on 18 publicly available datasets and showed that the cascaded algorithms achieved considerable speed-ups over the original AdaBoost.M2, AdaBoost.OC and AdaBoost.ECC in both training and detection runtimes. The cascaded classifiers did not exhibit significant compromises in their generalization ability and in fact produced evidence of improved accuracies on datasets with biased-class distributions.