The development of models for learning music similarity from audio media files is an
increasingly important task for the entertainment industry. This work proposes a novel music …

In this paper, we present a comprehensive study on the problem of finding the minimum
connector between two convex sets, particularly focusing on polytopes, and extended to …

One-against-one and one-against-all are common approaches to break down multiclass
classification problems into binary classification problems and build a multiclass classifier …

Classification is an essential task in the field of machine learning, where finding a maximum
margin classifier is one of its central problems. In this work, an evolutionary algorithm is …

In this paper we propose the development of an approach capable of improving the results
obtained by classification algorithms when applied to imbalanced datasets. The method …

This paper presents a novel adaptive one-pass Passive-Aggressive Radial Basis Function
(APARBF) for classification problems. The APARBF uses elliptic Gaussian neurons followed …

In this paper we propose a novel feature selection method for binary classification problems
based on an ordered search process to explore the space of feature subsets. The method …

This work presents a large margin learning algorithm for single hidden layer feedforward
networks (SLFNs) with random weights for the hidden neurons, called RP-IMA. This …

Esta dissertação explorou a relevância dos classificadores de margem larga no campo do
aprendizado de máquina. Buscou-se observar algumas características relevantes desses …

The development of models for learning music similarity and feature extraction from audio
media files is an increasingly important task for the entertainment industry. This work …