Due to growing expectations for quality and safety of food products and an increasing demand for more transparent food supply chains, pork production faces new challenges. In the course of this development, chain-wide communication has gained importance in agribusiness. Nonetheless, existing approaches focus primarily on information supply, whereas the use of information for managerial decision...

Exact logistic regression has become an important analytical technique, especially in the pharmaceutical industry, since the usual asymptotic methods for analyzing small, skewed, or sparse data sets are unreliable. Inference based on enumerating the exact distributions of sufficient statistics for parameters of interest in a logistic regression model, conditional on the remaining parameters, is...

This paper reports on the development of a learning system for the prediction of dichotomous response variables by combining fuzzy concept with classical regression technique. The algorithm involves linear transformation followed by linear programming. In the algorithm presented it was assumed that the logarithm of the odds (logit) is linearly related to X’s, the independent variables after und...

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...

The complex relationships of a crop with the pest, its natural enemies, and the climate factors exist in all the ecosystems, but the mathematic models has studied only some components to know the relation cause-effect. The most studied system has been concerned with the relationship pestnatural enemies such as prey-predator or host-parasitoid. The present paper shows a dynamical system for stud...

We introduce intervention optimization as a new area of exploration for data mining research. Interventions are events designed to impact a corresponding time series. The task is to maximize the impact of such events by training a model on historical data. We propose PRIMER as a new regression-rule learning system for identifying sets of event features that maximize impact. PRIMER is for use wh...

Engel curves are often estimated within a linear, or at least approximately linear system of equations. However, Engel curves are required to lie on or within the unit interval, while summing to unity. These restrictions are not easily accommodated within a linear system. Therefore, we apply the fractional multinomial logit model in our estimation of expenditure shares, because it more readily ...

In this paper, we describe a Parameter Server based Multiple Additive Regression Trees system, or PSMART for short. Empirically, PSMART scales MART to hundreds of billions of samples and thousands of features in production.

This work proposes an approach to collaborative tag recommendation based on a machine learning system for probabilistic regression. The goal of the method is to support users of current social network systems by providing a rank of new meaningful tags for a resource. This system provides a ranked tag set and it feeds on different posts depending on the resource for which the recommendation is r...