Modeling and Algorithms
The main objective of this chapter is to explain the machine learning concepts, mainly modeling and algorithms; batch learning and online learning; and supervised learning (regression and classification) and unsupervised learning (clustering) using examples. Modeling and algorithms will be explained based on the domain division characteristics, batch learning and online learning will be explained based on the availability of the data domain, and supervised learning and unsupervised learning will be explained based on the labeling of the data domain. This objective will be extended to the comparison of the mathematical models, hierarchical models, and layered models, using programming structures, such as control structures, modularization, and sequential statements.
- 1.T. G. Dietterich, “Machine-learning research: Four current directions,” AI Magazine, vol. 18, no. 4, pp. 97–136,1997.Google Scholar
- 6.O. Okun, and G. Valentini (Eds.), “Supervised and unsupervised ensemble methods and their applications,” Studies in Computational Intelligence series, vol. 126, 2008.Google Scholar
- 7.M. Ji, T. Yang, B. Lin, R. Jin, and J. Han. “A simple algorithm for semi-supervised learning with improved generalization error bound,” in Proceedings of the 29th International Conference on Machine Learning, pp. 1223–1230, 2012.Google Scholar
- 8.M.G. Lagoudakis and R. Parr. “Reinforcement learning as classification: Leveraging modern classifiers,” in Proceedings of the 20th International Conference on Machine Learning, vol. 3, pp. 424–431, 2003.Google Scholar
- 12.G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov. “Improving neural networks by preventing co-adaptation of feature detectors,” arXiv preprint arXiv:1207.0580, 2012.Google Scholar
- 14.O. L. Mangasarian and D. R. Musicant. 2000. “LSVM Software: Active set support vector machine classification software.” Available online at http://research.cs.wisc.edu/dmi/lsvm/.
- 15.M. Dunbar, J. M. Murray, L. A. Cysique, B. J. Brew, and V. Jeyakumar. “Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment.” European Journal of Operational Research 206(2): pp. 470–478, 2010.zbMATHCrossRefGoogle Scholar
- 16.V. Jeyakumar, G. Li, and S. Suthaharan. “Support vector machine classifiers with uncertain knowledge sets via robust optimization.” Optimization, pp. 1–18, 2012.Google Scholar
- 18.V. Franc, and V. Hlavac. “Multi-class support vector machine.” In Proceedings of the IEEE 16th International Conference on Pattern Recognition, vol. 2, pp. 236–239, 2002.Google Scholar
- 21.L. Wan, M. Zeiler, S. Zhang, Y. LeCun, and R. Fergus. “Regularization of neural networks using dropconnect.” In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 1058–1066, 2013.Google Scholar
- 22.W. Tu, and S. Sun, “Cross-domain representation-learning framework with combination of class-separate and domain-merge objectives,” In: Proceedings of the CDKD 2012 Conference, pp. 18–25, 2012.Google Scholar