Modeling the organoleptic properties of matured wine distillates

Authors:
S. B. Kotsiantis;G. E. Tsekouras;C. Raptis;P. E. Pintelas
Affiliations:
Educational Software Development Laboratory, Department of Mathematics, University of Patras, Greece;Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece;S&E&A METAXA Distilleries, Athens, S. A., Greece;Educational Software Development Laboratory, Department of Mathematics, University of Patras, Greece
Venue:
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2005

Citing 6
Cited 0

Locally Weighted Learning

Artificial Intelligence Review - Special issue on lazy learning
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Using analytic QP and sparseness to speed training of support vector machines

Proceedings of the 1998 conference on Advances in neural information processing systems II
Machine Learning

Machine Learning
A Simple Approach to Ordinal Classification

EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Improvements to the SMO algorithm for SVM regression

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present how the supervised machine learning techniques can be used to predict quality characteristics in an important chemical engineering application: the wine distillate maturation process. A number of experiments have been conducted with six regression-based algorithms, where the M5' algorithm was proved to be the most appropriate for predicting the organoleptic properties of the matured wine distillates. The rules that are exported by the algorithm are as accurate as human expert's decisions.