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 …

A neural network recognition and tracking system is proposed for classification of radar
pulses in autonomous Electronic Support Measure systems. Radar type information is …

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 …

This chapter introduces the basics of neural-symbolic systems used thoughout the book. A
brief bibliographical review is also presented. Neural-symbolic systems have become a very …

For pt. I see ibid., p. 775-85. In part I an equivalence between the concepts of fuzzy
clustering and soft competitive learning in clustering algorithms is proposed on the basis of …

This article provides a large-scale investigation into several of the properties of mixture-
model clustering techniques (also referred to as latent class cluster analysis, latent profile …

In modern manufacturing enterprises, quality-related soft sensors are important in
production, especially for batch manufacturing processes. In practice, batch processes …

Part I of this paper proposes a definition of the adaptive resonance theory (ART) class of
constructive unsupervised on-line learning clustering networks. Class ART generalizes …

An iterative block training method for support vector classifiers (SVCs) based on weighted
least squares (WLS) optimization is presented. The algorithm, which minimizes structural …

Tool condition monitoring is paramount for guaranteeing the quality of the workpiece and
improving the life time of the cutter. Force sensor has been proven to be one of the most …