Performance of Mechanical Air Classifiers

Ultimate performance of QEM classifiers

Supervised learning of classifiers often resorts to the minimization of a quadratic error, even if this criterion is more especially matched to nonlinear regression problems. It is shown that the mapping built by a quadratic error minimization (QEM) tends to output the Bayesian discriminating rules even with nonuniform losses, provided the desired responses are chosen accordingly. This property...

Performance evaluation of binary classifiers

We frequently design systems to detect events of interest, such as diseases in patients, faces in images, spam in an email queue, etc. We often want to compare the performance of such systems. One way to do this is to use a ROC (receiver operating characteristic) curve. This is a way of evaluating expected loss using a range of possible loss functions, where we vary the ratio of costs of false ...

Mechanical Behavior of Concrete, Made with Micro-Nano Air Bubbles

Nano materials have been widely used in laboratory and industrial scales in order to improve various properties of concrete and concrete mixture. The mainstream practice of the researches in this field is to add metallic nano-particles into the concrete mixture. The present research focuses on adding Micro-Nano Air Bubbles (MNAB) into water before mixing it with aggregate and cement mixtures. I...

Judging the Performance of Classifiers

A research on classification performance of fuzzy classifiers based on fuzzy set theory

Due to the complexities of objects and the vagueness of the human mind, it has attracted considerable attention from researchers studying fuzzy classification algorithms. In this paper, we propose a concept of fuzzy relative entropy to measure the divergence between two fuzzy sets. Applying fuzzy relative entropy, we prove the conclusion that patterns with high fuzziness are close to the classi...