Random decision tree is an ensemble of decision trees. The feature at any node of a tree in the ensemble is chosen randomly from remaining features. A chosen discrete feature on a...
Abstract. Influence of projection pursuit on classification errors and estimates of a posteriori probabilities from the sample is considered. Observed random variable is supposed t...
For many supervised learning tasks it is very costly to produce training data with class labels. Active learning acquires data incrementally, at each stage using the model learned...
We introduce a novel approach for magnetic resonance image (MRI) brain tissue classification by learning image neighborhood statistics from noisy input data using nonparametric den...
Tolga Tasdizen, Suyash P. Awate, Ross T. Whitaker,...
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...