Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Support Vector Machines and the Bayes Rule in Classification
Data Mining and Knowledge Discovery
Support vector machines are universally consistent
Journal of Complexity
Covering number bounds of certain regularized linear function classes
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Greedy algorithms for classification—consistency, convergence rates, and adaptivity
The Journal of Machine Learning Research
Sparseness of support vector machines
The Journal of Machine Learning Research
On the rate of convergence of regularized boosting classifiers
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
The Journal of Machine Learning Research
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
Applying the multi-category learning to multiple video object extraction
Pattern Recognition
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
On the Consistency of Multiclass Classification Methods
The Journal of Machine Learning Research
VC Theory of Large Margin Multi-Category Classifiers
The Journal of Machine Learning Research
ABC-boost: adaptive base class boost for multi-class classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A framework for kernel-based multi-category classification
Journal of Artificial Intelligence Research
Semisupervised multicategory classification with imperfect model
IEEE Transactions on Neural Networks
Multiclass support vector machines for adaptation in MIMO-OFDM wireless systems
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
The Journal of Machine Learning Research
Subset ranking using regression
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Annealed discriminant analysis
ECML'05 Proceedings of the 16th European conference on Machine Learning
On the consistency of multiclass classification methods
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Collaborative ranking: a case study on entity linking
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Prediction of the Amount of Wood Using Neural Networks
Journal of Mathematical Modelling and Algorithms
A generic model of multi-class support vector machine
International Journal of Intelligent Information and Database Systems
Multicategory large-margin unified machines
The Journal of Machine Learning Research
Hi-index | 0.00 |
The purpose of this paper is to investigate statistical properties of risk minimization based multi-category classification methods. These methods can be considered as natural extensions of binary large margin classification. We establish conditions that guarantee the consistency of classifiers obtained in the risk minimization framework with respect to the classification error. Examples are provided for four specific forms of the general formulation, which extend a number of known methods. Using these examples, we show that some risk minimization formulations can also be used to obtain conditional probability estimates for the underlying problem. Such conditional probability information can be useful for statistical inferencing tasks beyond classification.