Machine Learning - Special issue on learning with probabilistic representations
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
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
Eighteenth national conference on Artificial intelligence
Efficient discriminative learning of Bayesian network classifier via boosted augmented naive Bayes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Discriminative versus generative parameter and structure learning of Bayesian network classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
Discriminative mixture weight estimation for large Gaussian mixture models
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Performance analysis of time-distance gait parameters under different speeds
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Learning mixtures of DAG models
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Hi-index | 0.00 |
We consider the problem of learning density mixture models for classification. Traditional learning of mixtures for density estimation focuses on models that correctly represent the density at all points in the sample space. Discriminative learning, on the other hand, aims at representing the density at the decision boundary. We introduce a novel discriminative learning method for mixtures of generative models. Unlike traditional discriminative learning methods that often resort to computationally demanding gradient search optimization, the proposed method is highly efficient as it reduces to generative learning of individual mixture components on weighted data. Hence it is particularly suited to domains with complex component models, such as hidden Markov models or Bayesian networks in general, that are usually too complex for effective gradient search. We demonstrate the benefits of the proposed method in a comprehensive set of evaluations on time-series sequence classification problems.