We present some greedy learning algorithms for building sparse nonlinear regression and classification models from observational data using Mercer kernels. Our objective is to dev...
Prasanth B. Nair, Arindam Choudhury 0002, Andy J. ...
We consider the problem of dimensionality reduction, where given high-dimensional data we want to estimate two mappings: from high to low dimension (dimensionality reduction) and f...
Abstract. Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The...
In this paper we propose a new nonparametric regression algorithm based on Fuzzy systems with overlapping concepts. We analyze its consistency properties, showing that it is capab...
Genetic programming has been considered a promising approach for function approximation since it is possible to optimize both the functional form and the coefficients. However, it...