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Found 10 papers, 0 papers with code
Functional Mixtures-of-Experts
We extend the modeling with Mixtures-of-Experts (ME), as a framework of choice in modeling heterogeneity in data for prediction with vectorial observations, to this functional data analysis context.
Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review
There has been increasing attention to semi-supervised learning (SSL) approaches in machine learning to forming a classifier in situations where the training data for a classifier consists of a limited number of classified observations but a much larger number of unclassified observations.
Harmless label noise and informative soft-labels in supervised classification
In the framework of model-based classification, a simple, but key observation is that when the manual labels are sampled using the posterior probabilities of class membership, the noisy labels are as valuable as the ground-truth labels in terms of statistical information.
Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts
Mixture of experts (MoE) has a well-principled finite mixture model construction for prediction, allowing the gating network (mixture weights) to learn from the predictors (explanatory variables) together with the experts' network (mixture component densities).
Estimation of Classification Rules from Partially Classified Data
For class-conditional distributions taken to be known up to a vector of unknown parameters, the aim is to estimate the Bayes' rule of allocation for the allocation of subsequent unclassified observations.
Deep Gaussian Mixture Models
Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships.
Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach
Support vector machines (SVMs) are an important tool in modern data analysis.
A Universal Approximation Theorem for Mixture of Experts Models
The mixture of experts (MoE) model is a popular neural network architecture for nonlinear regression and classification.
Supervised Classification of Flow Cytometric Samples via the Joint Clustering and Matching (JCM) Procedure
We consider the use of the Joint Clustering and Matching (JCM) procedure for the supervised classification of a flow cytometric sample with respect to a number of predefined classes of such samples.
Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data
Simultaneously, JCM fits a random-effects model to construct an overall batch template -- used for registering populations across samples, and classifying new samples.
