IV. Daftar Pustaka
Gerami, F. (2015). Machine Learning Optimization Model Using Green’s Theorem. International Journal Of Humanities And Cultural Studies, 2085-2090.
[1]Olkopf, B., and Smola, A.J. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.
[2]Shalev, S., and Srebro, N. (2008). Svm optimization: inverse dependence on training set size.In ICML.
[3]Xu, L., Neufeld, J., Larson, B., and Schuurmans, D. (2004). Maximum margin clustering. Advances in Neural Information Processing Systems.
[4]Yang., Q., Zou, H.Y., Zhang, Y., Tang, L.J., et al. (2016). Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm. Talanta, 147, 609-614.
[5]Aich, U. and Banerjee, S. (2016). Application of teaching learning based optimization procedure for the development of SVM learned EDM process and its pseudo Pareto optimization. Applied Soft Computing, 39, 64-83.
[6]Linn, K.A., Gaonkar, B., Satterthwaite, T.D., Doshi, J., et al. (2016). Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine. NeuroImage, 132, 157-166.
[7]Ebrahimi, A. and Khamehchi, E. (2016). Developing a novel workflow for natural gas lift optimization using advanced support vector machine. Journal of Natural Gas Science and Engineering, 28, 626-638.
[8]Mas–Colell, W. and Green, S. (1995). Microeconomic Theory. Oxford University Press.