Regression refers to the problem of approximating measured data that are assumed to be produced by an underlying, possibly noisy function. However, in real applications the assumption that the data represent samples from one function is sometimes wrong. For instance, in process control different strategies might be used to achieve the same goal. Any regression model, trying to fit such data as ...

This report presents a SVM like learning system to handle multi-label problems. Such problems arise naturally in bio-informatics. Consider for instance the MIPS Yeast genome database in [12], it is formed by around 3,300 genes associated to their functional classes. One gene can have many classes, and different genes do not belong to the same number of functional categories. Such a problem can ...

In manipulating data such as in supervised learning, we often extract new features from the original input variables for the purpose of reducing the dimensions of input space and achieving better performances. In this paper, we show how standard algorithms for independent component analysis (ICA) can be extended to extract attributes for regression problems. The advantage is that general ICA al...

The amount of available data increases rapidly. This trend, often related to as Big Data challenges modern data mining algorithms, requiring new methods that can cope with very large, multi-variate regression problems. A promising approach that can tackle non-linear, higher-dimensional problems is regression using sparse grids. Sparse grids use a multiscale system of grids with basis functions ...

We present a generalisation of the sparse grid combination technique for regression in moderately high dimensions d ≤ 15. In contrast to the original combination technique the coefficients in the combination formula do not depend only on the used partial grids, but instead on the function to be reconstructed, i.e., on the given data. The coefficients are computed to fulfill a certain optimality...

Bagging, boosting and random subspace methods are well known re-sampling ensemble methods that generate and combine a diversity of learners using the same learning algorithm for the base-regressor. In this work, we built an ensemble of bagging, boosting and random subspace methods ensembles with 8 sub-regressors in each one and then an averaging methodology is used for the final prediction. We ...

Posterior consistency can be thought of as a theoretical justification of the Bayesian method. One of the most popular approaches to nonparametric Bayesian regression is to put a nonparametric prior distribution on the unknown regression function using Gaussian processes. In this paper, we study posterior consistency in nonparametric regression problems using Gaussian process priors. We use an ...

The aim of this research is to develop an autonomous system for solving data analysis problems. system, called Genetic Programming-Autonomous Solver (GP-AS) contains most of the features requ by an autonomous software: it decides if it knows or not how to solve a particular problem, it construct solutions for new problems, it can store the created solutions for later use, it can improve existin...

the main purposes of this study were to investigate health problems and their associated risk factors among employees of iranian petrochemical industries. this cross-sectional study was carried out at 21 iranian petrochemical companies. study population consisted of 3580 workers (including 44.2% shift workers and 55.8% day workers). data on personal details, shift schedule and health problems w...

We use reformulation techniques to model and solve a complex sphere covering problem occurring in the configuration of a gamma ray machine radiotherapy equipment unit.