In machine learning, active learning refers to algorithms that autonomously select the data
points from which they will learn. There are many data mining applications in which large …

In this work, we present a review of the state of the art of learning vector quantization (LVQ)
classifiers. A taxonomy is proposed which integrates the most relevant LVQ approaches to …

It is already true that Big Data has drawn huge attention from researchers in information
sciences, policy and decision makers in governments and enterprises. As the speed of …

The conformal predictions framework is a recent development in machine learning that can
associate a reliable measure of confidence with a prediction in any real-world pattern …

Support vector machines (SVMs) are a popular class of supervised learning algorithms, and
are particularly applicable to large and high-dimensional classification problems. Like most …

This book intends to provide a comprehensive overview on different aspects of mechanisms
and techniques for information security. It is written for students, researchers, and …

In this paper, we propose two active learning algorithms for semiautomatic definition of
training samples in remote sensing image classification. Based on predefined heuristics, the …

This paper presents an active learning method which reduces the labeling effort of domain
experts in multi-class classification problems. Active learning is applied in conjunction with …

This paper proposes a new active learning approach, confidence-based active learning, for
training a wide range of classifiers. This approach is based on identifying and annotating …

This valuable text addresses different pattern recognition (PR) tasks in a unified framework
with both theoretical and experimental results. Tasks covered include data condensation …