Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
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
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
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Machine Learning
Pattern Recognition Letters
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
The Journal of Machine Learning Research
One-class svms for document classification
The Journal of Machine Learning Research
Support Vector Data Description
Machine Learning
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Using One-Class and Two-Class SVMs for Multiclass Image Annotation
IEEE Transactions on Knowledge and Data Engineering
Multiclass Boosting for Weak Classifiers
The Journal of Machine Learning Research
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Neural Computation
Multiclass boosting with repartitioning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Using boosting to prune bagging ensembles
Pattern Recognition Letters
Unifying multi-class AdaBoost algorithms with binary base learners under the margin framework
Pattern Recognition Letters
Boosting recombined weak classifiers
Pattern Recognition Letters
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Geodesic analysis on the gaussian RKHS hypersphere
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Clustering-based ensembles for one-class classification
Information Sciences: an International Journal
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AdaBoost.M1 has been successfully applied to improve the accuracy of a learning algorithm for multi-class classification problems. However, it may be hard to satisfy the required conditions in some practical cases. An improved algorithm called AdaBoost.MK is developed to solve this problem. Early proposed support vector machines (SVM)-based, multi-class classification algorithms work by splitting the original problem into a set of two-class subproblems. The amount of time and space required by these algorithms is very demanding. We develop a multi-class classification algorithm by incorporating one-class SVMs with a well-designed discriminant function. Finally, a hybrid method integrating AdaBoost.MK and one-class SVMs is proposed to solve multi-class classification problems. Experimental results on data sets from UCI and Statlog show that the proposed approach outperforms other multi-class algorithms, such as support vector data descriptions (SVDDs) and AdaBoost.M1 with one-class SVMs, and the improvement is found to be statistically significant.