000			04259cam a2200397 a 4500
008			100316s2009 nyua b 001 0 eng
010			_a2009930499
020			_a9780387981345
020			_a0387981349
035			_a(Sirsi) u5139
040			_aEG-CaNU _c EG-CaNU _d EG-CaNU
042			_ancode
082	0	4	_a006.31 _2 22
100	1		_aClarke, Bertrand. _910264
245	1	0	_aPrinciples and theory for data mining and machine learning / _c Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang.
250			_a1st ed.
260			_aNew York : _b Springer, _c 2009.
300			_axv, 781 p. : _b ill. ; _c 24 cm.
490			_aSpringer series in statistics
504			_aIncludes bibliographical references and index.
505	0		_aVariability, information, prediction.- Kernel smoothing.- Spline smoothing.- New wave nonparametrics.- Supervised learning: Partition methods.- Alternative nonparametrics.- Computational comparisons.- Unsupervised learning: Clustering.- Learning in high dimensions.- Variable selection.- Multiple testing.
520	3		_aThis book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression, classification, and ensemble methods. The final chapters focus on clustering, dimension reduction, variable selection, and multiple comparisons. All these topics have undergone extraordinarily rapid development in recent years and this treatment offers a modern perspective emphasizing the most recent contributions. The presentation of foundational results is detailed and includes many accessible proofs not readily available outside original sources. While the orientation is conceptual and theoretical, the main points are regularly reinforced by computational comparisons. Intended primarily as a graduate level textbook for statistics, computer science, and electrical engineering students, this book assumes only a strong foundation in undergraduate statistics and mathematics, and facility with using R packages. The text has a wide variety of problems, many of an exploratory nature.
520			_aThere are numerous computed examples, complete with code, so that further computations can be carried out readily. The book also serves as a handbook for researchers who want a conceptual overview of the central topics in data mining and machine learning. Bertrand Clarke is a Professor of Statistics in the Department of Medicine, Department of Epidemiology and Public Health, and the Center for Computational Sciences at the University of Miami. He has been on the Editorial Board of the Journal of the American Statistical Association, the Journal of Statistical Planning and Inference, and Statistical Papers. He is co-winner, with Andrew Barron, of the 1990 Browder J. Thompson Prize from the Institute of Electrical and Electronic Engineers. Ernest Fokoue is an Assistant Professor of Statistics at Kettering University. He has also taught at Ohio State University and been a long term visitor at the Statistical and Mathematical Sciences Institute where he was a Post-doctoral Research Fellow in the Data Mining and Machine Learning Program. In 2000, he was the winner of the Young Researcher Award from the International Association for Statistical Computing. Hao Helen Zhang is an Associate Professor of Statistics in the Department of Statistics at North Carolina State University. For 2003-2004, she was a Research Fellow at SAMSI and in 2007, she won a Faculty Early Career Development Award from the National Science Foundation. She is on the Editorial Board of the Journal of the American Statistical Association and Biometrics.
650		0	_aData mining. _910265
650		0	_aMachine learning _x Statistical methods. _9107
653			_aModel uncertainty
653			_aRegularization methods
653			_ahigh dimensional and complex data
653			_anonlinear methods
653			_a pattern recognition
700	1		_aFokoué, Ernest. _910266
700	1		_aZhang, Hao Helen. _95196
920			_a9780387981352
596			_a1
999			_c4144 _d4144