Functional data analysis: classification and regression

[1]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[2]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[4]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[5]  G. Wahba Spline models for observational data , 1990 .

[6]  Bernhard Schölkopf,et al.  The connection between regularization operators and support vector kernels , 1998, Neural Networks.

[7]  Michael Vogt,et al.  SMO Algorithms for Support Vector Machines without Bias Term , 2002 .

[8]  J L Wang,et al.  Dual modes of aging in Mediterranean fruit fly females. , 1998, Science.

[9]  D. Whittaker,et al.  A Course in Functional Analysis , 1991, The Mathematical Gazette.

[10]  Peter Hall,et al.  A Functional Data—Analytic Approach to Signal Discrimination , 2001, Technometrics.

[11]  Harris Drucker,et al.  Support vector machines for spam categorization , 1999, IEEE Trans. Neural Networks.

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[13]  S. Gunn Support Vector Machines for Classification and Regression , 1998 .

[14]  Martin Alexander Youngson,et al.  Linear Functional Analysis , 2000 .

[15]  Thorsten Joachims,et al.  Making large-scale support vector machine learning practical , 1999 .

[16]  Ulrich Stadtmüller,et al.  Generalized functional linear models , 2005 .

[17]  Thorsten Joachims,et al.  Estimating the Generalization Performance of an SVM Efficiently , 2000, ICML.

[18]  Michael W. Trosset The Krigifier: A Procedure for Generating Pseudorandom Nonlinear Objective Functions for Computational Experimentation , 1999 .

[19]  J. Vaupel,et al.  Reproductive potential predicts longevity of female Mediterranean fruitflies , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[20]  Gareth M. James,et al.  Functional linear discriminant analysis for irregularly sampled curves , 2001 .

[21]  B. Silverman,et al.  Functional Data Analysis , 1997 .

[22]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[23]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..