Support vector machines for classification and regression.

The increasing interest in Support Vector Machines (SVMs) over the past 15 years is described. Methods are illustrated using simulated case studies, and 4 experimental case studies, namely mass spectrometry for studying pollution, near infrared analysis of food, thermal analysis of polymers and UV/visible spectroscopy of polyaromatic hydrocarbons. The basis of SVMs as two-class classifiers is shown with extensive visualisation, including learning machines, kernels and penalty functions. The influence of the penalty error and radial basis function radius on the model is illustrated. Multiclass implementations including one vs. all, one vs. one, fuzzy rules and Directed Acyclic Graph (DAG) trees are described. One-class Support Vector Domain Description (SVDD) is described and contrasted to conventional two- or multi-class classifiers. The use of Support Vector Regression (SVR) is illustrated including its application to multivariate calibration, and why it is useful when there are outliers and non-linearities.

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

[2]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[3]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[4]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[5]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[6]  S. Wold,et al.  Partial least squares analysis with cross‐validation for the two‐class problem: A Monte Carlo study , 1987 .

[7]  G. Dunteman.  Principal Components Analysis , 1989 .

[8]  Jerome H. Friedman,et al.  Classification: Oldtimers and newcomers , 1989 .

[9]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[10]  R. Brereton.  Multilevel multifactor designs for multivariate calibration , 1997 .

[11]  Richard G. Brereton,et al.  Introduction to multivariate calibration in analytical chemistry , 2000 .

[12]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[13]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[14]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[15]  Guodong Guo,et al.  Support vector machines for face recognition , 2001, Image Vis. Comput..

[16]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[17]  A. Belousov,et al.  A flexible classification approach with optimal generalisation performance: support vector machines , 2002 .

[18]  Francis Eng Hock Tay,et al.  Modified support vector machines in financial time series forecasting , 2002, Neurocomputing.

[19]  Richard G. Brereton,et al.  Chemometrics: Data Analysis for the Laboratory and Chemical Plant , 2003 .

[20]  Giovanni Luca Christian Masala,et al.  A comparative study of K-Nearest Neighbour, Support Vector Machine and Multi-Layer Perceptron for Thalassemia screening , 2003 .

[21]  M. Barker,et al.  Partial least squares for discrimination , 2003 .

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

[23]  R. Brereton,et al.  Active learning support vector machines for optimal sample selection in classification , 2004 .

[24]  L. Buydens,et al.  Comparing support vector machines to PLS for spectral regression applications , 2004 .

[25]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[26]  Melissa Hanna-Brown,et al.  Toxicological classification of urine samples using pattern recognition techniques and capillary electrophoresis , 2004, Analytical and bioanalytical chemistry.

[27]  Robert P. W. Duin,et al.  Support Vector Data Description , 2004, Machine Learning.

[28]  L. Buydens,et al.  Multivariate calibration with least-squares support vector machines. , 2004, Analytical chemistry.

[29]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[30]  B. Walczak,et al.  About kernel latent variable approaches and SVM , 2005 .

[31]  R. Brereton,et al.  Pattern recognition for the analysis of polymeric materials. , 2006, The Analyst.

[32]  Yun Xu,et al.  Support Vector Machines: A Recent Method for Classification in Chemometrics , 2006 .

[33]  R. Brereton.  Consequences of sample size, variable selection, and model validation and optimisation, for predicting classification ability from analytical data , 2006 .

[34]  Richard G. Brereton,et al.  Learning Vector Quantization for Multiclass Classification: Application to Characterization of Plastics , 2007, J. Chem. Inf. Model..

[35]  L. Buydens,et al.  Visualisation and interpretation of Support Vector Regression models. , 2007, Analytica chimica acta.

[36]  R. Brereton,et al.  Dynamic mechanical analysis and chemometrics for polymer identification , 2007 .

[37]  Richard G. Brereton,et al.  Pattern recognition and feature selection for the discrimination between grades of commercial plastics , 2007 .

[38]  Richard G. Brereton,et al.  Pattern Recognition of Gas Chromatography Mass Spectrometry of Human Volatiles in Sweat to distinguish the sex of subjects and determine potential Discriminatory Marker Peaks , 2007 .

[39]  Richard G Brereton,et al.  Self Organising Maps for distinguishing polymer groups using thermal response curves obtained by dynamic mechanical analysis. , 2008, The Analyst.

[40]  Richard G. Brereton,et al.  Chemometrics for Pattern Recognition , 2009 .

[41]  R. Brereton,et al.  Comparison of performance of five common classifiers represented as boundary methods: Euclidean Distance to Centroids, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Learning Vector Quantization and Support Vector Machines, as dependent on data structure , 2009 .

[42]  Shigeo Abe.  Support Vector Machines for Pattern Classification , 2010, Advances in Pattern Recognition.