The nature of statistical learning theory
The nature of statistical learning theory
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Generative versus Discriminative Methods for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Low-frequency vocal modulations in vowels produced by Parkinsonian subjects
Speech Communication
Adaptive mixtures of local experts
Neural Computation
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Enhancing interactive image segmentation with automatic label set augmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Classification with Incomplete Data Using Dirichlet Process Priors
The Journal of Machine Learning Research
Dirichlet Process Mixtures of Generalized Linear Models
The Journal of Machine Learning Research
Classification of the action surface EMG signals based on the dirichlet process mixtures method
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I
Nonparametric Bayes classification and hypothesis testing on manifolds
Journal of Multivariate Analysis
Expert Systems with Applications: An International Journal
Multiview hierarchical bayesian regression model andapplication to online advertising
Proceedings of the 21st ACM international conference on Information and knowledge management
Expert Systems with Applications: An International Journal
An Ensemble Topic Model for Sharing Healthcare Data and Predicting Disease Risk
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Protein fold recognition with a two-layer method based on SVM-SA, WP-NN and C4.5 TLM-SNC
International Journal of Data Mining and Bioinformatics
Expert Systems with Applications: An International Journal
A new hybrid intelligent system for accurate detection of Parkinson's disease
Computer Methods and Programs in Biomedicine
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We introduce a new nonlinear model for classification, in which we model the joint distribution of response variable, y, and covariates, x, non-parametrically using Dirichlet process mixtures. We keep the relationship between y and x linear within each component of the mixture. The overall relationship becomes nonlinear if the mixture contains more than one component, with different regression coefficients. We use simulated data to compare the performance of this new approach to alternative methods such as multinomial logit (MNL) models, decision trees, and support vector machines. We also evaluate our approach on two classification problems: identifying the folding class of protein sequences and detecting Parkinson's disease. Our model can sometimes improve predictive accuracy. Moreover, by grouping observations into sub-populations (i.e., mixture components), our model can sometimes provide insight into hidden structure in the data.