Visual place recognition has attracted widespread research interest in multiple fields such
as computer vision and robotics. Recently, researchers have employed advanced deep …

Feature selection (FS) is an important data processing method in pattern recognition and
data mining. Due to not considering characteristics of the FS problem itself, traditional …

Ant colony optimization (ACO) is widely used in feature selection owing to its excellent
global/local search capabilities and flexible graph representation. However, the current ACO …

High dimensionality problem is an important concern for short text classification due to its
effect on computational cost and accuracy of classifiers. Also, short text data, besides being …

Machine learning-based intrusion detection system (IDS) is an important requirement for
securing data traffic in wireless mesh networks. The noisy and redundant features of network …

Many research topics present very high dimensional data. Because of the heavy execution
times and large memory requirements, many machine learning methods have difficulty in …

Epilepsy is a group of neurological disorders that affect normal brain activities and human
behavior. Electroencephalogram based automatic epileptic seizure detection has significant …

Feature Selection for mixed data is an active research area with many applications in
practical problems where numerical and non-numerical features describe the objects of …

Multi-scale decision system (MDS) is an effective tool to describe hierarchical data in
machine learning. Optimal scale combination (OSC) selection and attribute reduction are …

In this paper, a theoretical learning curve is derived for the multi-class Bayes classifier. This
curve fits general multivariate parametric models of the class-conditional probability density …