An intelligent decision guidance system which is composed of data collection, learning, optimization, and prediction is proposed in the paper. Built on the traditional relational database management system, the regression learning ability is incorporated. The Expectation Maximization Multi-Step Piecewise Surface Regression Learning (EMMPSR) algorithm is proposed to solve piecewise surface regre...

Regression methods aim at inducing models of numeric data. While most state-of-the-art machine learning methods for regression focus on inducing piecewise regression models (regression and model trees), we investigate the predictive performance of regression models based on polynomial equations. We present Ciper, an efficient method for inducing polynomial equations and empirically evaluate its...

A new learning algorithm for solving piecewise linear regression problems is proposed. It is able to train a proper multilayer feedforward neural network so as to reconstruct a target function assuming a different linear behavior on each set of a polyhedral partition of the input domain. The proposed method combine local estimation, clustering in weight space, classification and regression in o...

Estimation of univariate regression functions from bounded i.i.d. data is considered. Estimates are deened by minimizing a complexity penalized residual sum of squares over all piecewise polynomials. The integrated squared error of these estimates achieves for piecewise p-smooth regression functions the rate (ln 2 (n)=n) 2p 2p+1 .

We derive an exact and efficient Bayesian regression algorithm for piecewise constant functions of unknown segment number, boundary locations, and levels. The derivation works for any noise and segment level prior, e.g. Cauchy which can handle outliers. We derive simple but good estimates for the in-segment variance. We also propose a Bayesian regression curve as a better way of smoothing data ...

We derive an exact and efficient Bayesian regression algorithm for piecewise constant functions of unknown segment number, boundary location, and levels. It works for any noise and segment level prior, e.g. Cauchy which can handle outliers. We derive simple but good estimates for the in-segment variance. We also propose a Bayesian regression curve as a better way of smoothing data without blurr...