02344cam a2200229 i 4500008004100000020001800041040000800059050002200067100002200089245011600111300001900227490005200246505001700298518001500315520153200330650003401862650001901896650002501915650005401940650006601994650005402060131029s2014    njua     b    001 0 eng    a9780470384428  aSDU00aHG176bK63 M 20141 aKlemelä, Jussi,10aMultivariate nonparametric regression and visualization :bwith R and applications to finance /cJussi Klemela.  a367 p. :bill.0 aWiley series in computational statistics ;v699  aHKBU library  aYT2025 M10  a"This book uniquely utilizes visualization tools to explain and study statistical learning methods. Covering classification and regression, the book is divided into two parts. First, various visualization methods are introduced and explained. Here, the reader is presented with applications of visualization techniques to learning samples (including projection pursuit, graphical matrices, and parallel coordinate plots) as well as functions, and sets. Next, the author provides a "toolbox" that contains formal definitions of the methods applied in the book and then proceeds to present visualizations of classified learning samples and classified test samples. Visualization methods are applied for the initial exploration of data, to identify the correct type of classifier, and to estimate the best achievable classification error. Once identified, the classifier's properties, proper uses, and overall performance are demonstrated and measured using visualization methods. Key areas of coverage include linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods In addition to providing applications to engineering and biomedicine, the author also uses financial data sets as real data examples to illustrate nonparametric function estimation. The author's own R software is used throughout to reproduce and modify the book's computations and research. Readers can duplicate these applications using the software, available via the book's related Web site"-- 0aFinancexMathematical models. 0aVisualization. 0aRegression analysis. 7aMATHEMATICS / Probability & Statistics / General. 7aMATHEMATICS / Probability & Statistics / Regression Analysis. 7aCOMPUTERS / Programming Languages / Visual BASIC.