000			02568cam a2200289zu 4500
001			88813009
003			FRCYB88813009
005			20250107211251.0
006			m o d
007			cr un
008			250107s2012 fr | o|||||0|0|||eng d
020			_a9780470195154
035			_aFRCYB88813009
040			_aFR-PaCSA _ben _c _erda
100	1		_aDehmer, Matthias
245	0	1	_aStatistical and Machine Learning Approaches for Network Analysis _c['Dehmer, Matthias', 'Basak, Subhash C. ']
264		1	_bJohn Wiley & Sons _c2012
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aDehmer, Matthias
700	0		_aBasak, Subhash C.
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88813009 _qtext/html _a
520			_aExplore 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 networksAn introduction to complex networks—measures, statistical properties, and modelsModeling for evolving biological networksThe structure of an evolving random bipartite graphDensity-based enumeration in structured dataHyponym 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.
999			_c61389 _d61389