Similarity-Based Clustering (eBook, PDF)

Recent Developments and Biomedical Applications
Redaktion: Villmann, Thomas; Verleysen, Michel; Hammer, Barbara; Biehl, M.

• Format: PDF

Produktbeschreibung

This book is the outcome of the Dagstuhl Seminar on "Similarity-Based Clustering" held at Dagstuhl Castle, Germany, in Spring 2007.

In three chapters, the three fundamental aspects of a theoretical background, the representation of data and their connection to algorithms, and particular challenging applications are considered. Topics discussed concern a theoretical investigation and foundation of prototype based learning algorithms, the development and extension of models to directions such as general data structures and the application for the domain of medicine and biology.

Similarity based methods find widespread applications in diverse application domains, including biomedical problems, but also in remote sensing, geoscience or other technical domains. The presentations give a good overview about important research results in similarity-based learning, whereby the character of overview articles with references to correlated research articles makes the contributions particularly suited for a first reading concerning these topics.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 203
• Erscheinungstermin: 14. Mai 2009
• Englisch
• ISBN-13: 9783642018053
• Artikelnr.: 44134166

Autorenporträt

Die vorliegende Arbeit entstand im wesentlichen am Institut für Informatik der Universität Leipzig in der Forschungsgruppe Neuroinformatik, der der Autor von 1991 bis 1994 angehörte. Sie wurde 1996 als Dissertationsschrift angenommen, der Betreuer war Dr. habil. Ralf Der. Neben der Theorie und Anwendung neuronaler Netze arbeitet der Autor auf den Gebieten Genetische Algorithmen, maschinelle Lernverfahren, Optimierung und Statistik.

Inhaltsangabe

I: Dynamics of Similarity-Based Clustering.- Statistical Mechanics of On-line Learning.- Some Theoretical Aspects of the Neural Gas Vector Quantizer.- Immediate Reward Reinforcement Learning for Clustering and Topology Preserving Mappings.- II: Information Representation.- Advances in Feature Selection with Mutual Information.- Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data.- Median Topographic Maps for Biomedical Data Sets.- Visualization of Structured Data via Generative Probabilistic Modeling.- III: Particular Challenges in Applications.- Learning Highly Structured Manifolds: Harnessing the Power of SOMs.- Estimation of Boar Sperm Status Using Intracellular Density Distribution in Grey Level Images.- HIV-1 Drug Resistance Prediction and Therapy Optimization: A Case Study for the Application of Classification and Clustering Methods.