Artificial intelligence techniques point out differences in classification performance between light and standard bovine carcasses

TL;DR: Conformation was an important trait in explaining variation between breeds because its values were positively correlated with muscular development and carcass compactness.

TL;DR: The possibility of designing a more objective and easy-to-interpret system to classify the most common types of carcass in the territory of the EU using only a few single attributes that are easily obtained in an industrial environment is confirmed.

TL;DR: Four machine learning algorithms were applied to predict six carcass traits and compared against linear regression prediction models and SMO was the best performing algorithm in most scenarios.

TL;DR: It is shown that it is possible to model sensory impressions of consumers about beef meat and that aging is the most important trait for improving consumers' appreciation of beef meat.

TL;DR: This survey reviews work in machine learning on methods for handling data sets containing large amounts of irrelevant information and describes the advances that have been made in both empirical and theoretical work in this area.

TL;DR: A class of weight-setting methods for lazy learning algorithms which use performance feedback to assign weight settings demonstrated three advantages over other methods: they require less pre-processing, perform better in the presence of interacting features, and generally require less training data to learn good settings.

TL;DR: In this article, a working party of the Study Commission on Cattle Production of the European Association for Animal Production has established reference methods for the assessment of carcass characteristics in cattle.

TL;DR: This paper illustrates with an example of how it is possible to clone the behaviour of bovine carcass classifiers, leading to possible further industrial applications.