Data analysis plays an indispensable role for understanding various phenomena. Cluster
analysis, primitive exploration with little or no prior knowledge, consists of research …

Applications of clustering algorithms in biomedical research are ubiquitous, with typical
examples including gene expression data analysis, genomic sequence analysis, biomedical …

In supervised machine learning, an agent is typically trained once and then deployed. While
this works well for static settings, robots often operate in changing environments and must …

When an agent acquires new information, ideally it would immediately be capable of using
that information to understand its environment. This is not possible using conventional deep …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

We introduce Learn++, an algorithm for incremental training of neural network (NN) pattern
classifiers. The proposed algorithm enables supervised NN paradigms, such as the …

This tutorial text provides the reader with an understanding of artificial neural networks
(ANNs), and their application, beginning with the biological systems which inspired them …

Principles derived from an analysis of experimental literatures in VISIOn, speech, cortical
development, and reinforcement learning, including attentional blocking and cognitive …

Clustering algorithms aim at modeling fuzzy (ie, ambiguous) unlabeled patterns efficiently.
Our goal is to propose a theoretical framework where the expressive power of clustering …

Clustering is a fundamental data analysis method. It is widely used for pattern recognition,
feature extraction, vector quantization (VQ), image segmentation, function approximation …