Building efficient fuzzy regression trees for large scale and high dimensional problems

Efficient algorithms for computing the best subset regression models for large-scale problems

Several strategies for computing the best subset regression models are proposed. Some of the algorithms are modified versions of existing regression-tree methods, while others are new. The first algorithm selects the best subset models within a given size range. It uses a reduced search space and is found to outperform computationally the existing branch-and-bound algorithm. The properties and ...

Methods for regression analysis in high-dimensional data

By evolving science, knowledge and technology, new and precise methods for measuring, collecting and recording information have been innovated, which have resulted in the appearance and development of high-dimensional data. The high-dimensional data set, i.e., a data set in which the number of explanatory variables is much larger than the number of observations, cannot be easily analyzed by ...

Covariance-regularized regression and classification for high-dimensional problems.

In recent years, many methods have been developed for regression in high-dimensional settings. We propose covariance-regularized regression, a family of methods that use a shrunken estimate of the inverse covariance matrix of the features in order to achieve superior prediction. An estimate of the inverse covariance matrix is obtained by maximizing its log likelihood, under a multivariate norma...

Support Vector Machines for Large-Scale Regression Problems

SVMTorch: Support Vector Machines for Large-Scale Regression Problems

Support Vector Machines (SVMs) for regression problems are trained by solving a quadratic optimization problem which needs on the order of l memory and time resources to solve, where l is the number of training examples. In this paper, we propose a decomposition algorithm, SVMTorch, which is similar to SVM-Light proposed by Joachims (1999) for classi cation problems, but adapted to regression p...