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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Statistical and Machine Learning Approaches for Network Analysis (Hardcover, New)
Loot Price: R3,494
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Statistical and Machine Learning Approaches for Network Analysis (Hardcover, New)
Expected to ship within 10 - 15 working days
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Explore the multidisciplinary nature of complex networks through
machine learning techniques Statistical and Machine Learning
Approaches for Network Analysis provides an accessible framework
for structurally analyzing graphs by bringing together known and
novel approaches on graph classes and graph measures for
classification. By providing different approaches based on
experimental data, the book uniquely sets itself apart from the
current literature by exploring the application of machine learning
techniques to various types of complex networks. Comprised of
chapters written by internationally renowned researchers in the
field of interdisciplinary network theory, the book presents
current and classical methods to analyze networks statistically.
Methods from machine learning, data mining, and information theory
are strongly emphasized throughout. Real data sets are used to
showcase the discussed methods and topics, which include: * A
survey of computational approaches to reconstruct and partition
biological networks * An introduction to complex networks measures,
statistical properties, and models * Modeling for evolving
biological networks * The structure of an evolving random bipartite
graph * Density-based enumeration in structured data * Hyponym
extraction employing a weighted graph kernel Statistical and
Machine Learning Approaches for Network Analysis is an excellent
supplemental text for graduate-level, cross-disciplinary courses in
applied discrete mathematics, bioinformatics, pattern recognition,
and computer science. The book is also a valuable reference for
researchers and practitioners in the fields of applied discrete
mathematics, machine learning, data mining, and biostatistics.
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