In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data...
Learning function relations or understanding structures of data lying in manifolds embedded in huge dimensional Euclidean spaces is an important topic in learning theory. In this ...
A vision-based machine learner is presented that learns characteristic hand and object movement patterns for using certain objects, and uses this information to recreate the "...
Andreya Piplica, Alexandra Olivier, Allison Petros...
We develop a cyclical blockwise coordinate descent algorithm for the multi-task Lasso that efficiently solves problems with thousands of features and tasks. The main result shows ...
Abstract--This paper presents local spline regression for semisupervised classification. The core idea in our approach is to introduce splines developed in Sobolev space to map the...