De‐migration‐based supervised learning for interpolation and regularization of 3D offset classes

An Adaptive Regularization Criterion for Supervised Learning

Semi-Supervised Learning Based on Semiparametric Regularization

Semi-supervised learning plays an important role in the recent literature on machine learning and data mining and the developed semisupervised learning techniques have led to many data mining applications in recent years. This paper addresses the semi-supervised learning problem by developing a semiparametric regularization based approach, which attempts to discover the marginal distribution of...

Semi-supervised Learning by Higher Order Regularization

In semi-supervised learning, at the limit of infinite unlabeled points while fixing labeled ones, the solutions of several graph Laplacian regularization based algorithms were shown by Nadler et al. (2009) to degenerate to constant functions with “spikes” at labeled points in R for d ≥ 2. These optimization problems all use the graph Laplacian regularizer as a common penalty term. In this paper...

Differential Geometric Regularization for Supervised Learning of Classifiers

We study the problem of supervised learning for both binary and multiclass classification from a unified geometric perspective. In particular, we propose a geometric regularization technique to find the submanifold corresponding to an estimator of the class probability P (y|x). The regularization term measures the volume of this submanifold, based on the intuition that overfitting produces rapi...

Covariance Regularization for Supervised Learning in High Dimensions

This paper studies the effect of covariance regularization for classification of high-dimensional data. This is done by fitting a mixture of Gaussians with a regularized covariance matrix to each class. Three data sets are chosen to suggest the results are applicable to any domain with high-dimensional data. The regularization needs of the data when pre-processed using the dimensionality reduct...