This report describes the approach developed by the JKU team for the MediaEval 2017 AcousticBrainz Genre Task. After experimenting with various classifiers on the development dataset, our final approach is based on multilayer perceptron classifiers.

We describe a multilayer perceptron model to predict the laboratory measurements of paper quality using the instantaneous state of the papermaking production process. Actual industrial data from a pilot paper machine was used. The final model met its goal accuracy 95.7% of the time at best (tensile index quality) and 66.7% at worst (beta formation). We anticipate usage possibilities in lowering...

A fast method for sizing the multilayer perceptron is proposed. The principal assumption is that a modular network with the same theoretical pattern storage as the multilayer perceptron has the same training error. This assumption is analyzed for the case of random patterns. Using several benchmark datasets, the validity of the approach is demonstrated.

over the last decade or so, artificial neural networks (anns) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. however, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. for this reason, this study aims at de...

Nowadays, breast cancer is the most frequent among women. Early detection a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, methods to improve accuracy of ML classification models for prognosis are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Se...

In this contribution we present an algorithm for using possibly inaccurate knowledge of model derivatives as a part of the training data for a multilayer perceptron network (MLP). In many practical process control problems there are many well-known rules about the eeect of control variables to the target variables. With the presented algorithm the basically data driven neural network model can ...