Page 117 - Data Science Algorithms in a Week

Evolutionary Optimization of Support Vector Machines … 101

Golberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning
reading. MA: Addison-Wiley, USA.
Goldberg, D. E. (1991). Real-coded genetic algorithms, virtual alphabets, and blocking.
Complex systems, 5(2), 139-167.
Gruber, F. K. (2004). Evolutionary Optimization of Support Vector Machines (Doctoral
dissertation, University of Central Florida Orlando, Florida).
Herbrich, R. (2001). Learning kernel classifiers: theory and algorithms. MIT Press.
Holland, J. H. (1975). Adaptation in natural and artificial systems. An introductory
analysis with application to biology, control, and artificial intelligence. Ann Arbor,
MI: University of Michigan Press.
Joachims, T. (2000). Estimating the generalization performance of a SVM efficiently.
Universität Dortmund.
John, P. (1998). How to implement SVM’s, Microsoft Research. IEEE Intelligent
Systems.
Kaufman, L. (1998). Solving the quadratic programming problem arising in support
vector classification. Advances in Kernel Methods-Support Vector Learning, 147-
167.
Keerthi, S. S., & Lin, C. J. (2003). Asymptotic behaviors of support vector machines with
Gaussian kernel. Neural computation, 15(7), 1667-1689.
Kohavi, R. (1995, August). A study of cross-validation and bootstrap for accuracy
estimation and model selection. In Ijcai (Vol. 14, No. 2, pp. 1137-1145).
Kuhn, M. (2008). Caret package. Journal of Statistical Software, 28(5), 1-26.
Law, A. M., Kelton, W. D., & Kelton, W. D. (1991). Simulation modeling and analysis
(Vol. 2). New York: McGraw-Hill.
Lendasse, A., Wertz, V., & Verleysen, M. (2003). Model selection with cross-validations
and bootstraps—application to time series prediction with RBFN models. Artificial
Neural Networks and Neural Information Processing—ICANN/ICONIP 2003, 174-
174.
Lessmann, S., Stahlbock, R., & Crone, S. F. (2006, July). Genetic algorithms for support
vector machine model selection. In Neural Networks, 2006. IJCNN'06. International
Joint Conference on (pp. 3063-3069). IEEE.
Martin, J., & Hirschberg, D. (1996). Small sample statistics for classification error rates
II: Confidence intervals and significance tests.
Mendenhall, W., & Sincich, T. (2016). Statistics for Engineering and the Sciences. CRC
Press.
Michalewicz, Z. (1996). Introduction. In Genetic Algorithms+ Data Structures=
Evolution Programs (pp. 1-10). Springer Berlin Heidelberg.
Mitchell, M. (1998). An introduction to genetic algorithms (complex adaptive systems).
Murison A. &. Wardell, “gaucho: Genetic Algorithms for Understanding Clonal
Heterogeneity and Ordering,” R package version 1.12.0, 2014.