There is a very large number of classification algorithms, including decision trees, instance-based learners, support vector machines, rule-based learners, neural networks, Bayesian networks. There also ways of combining them into ensemble classifiers such as boosting, bagging, stacking, and forests of trees.
To delve deeper into classifiers and their role in machine learning, a number of books are recommended covering machine learning (Bishop, 2007; Mitchell, 1997; Written & Frank, 2005) and artificial intelligence (Russell & Norvig, 2003) in general. Seminal papers on classifiers can be found in collections of papers on machine learning (Dietterich & Shavlik, 1990; Kodratoff & Michalski, 1990; Michalski, Carbonell & Mitchell, 1983, 1986).
Recommended Reading
Bishop, C. M. (2007). Pattern recognition and machine learning. New York: Springer.
Dietterich, T., & Shavlik, J. (Eds.). Readings in machine learning. San Mateo, CA: Morgan Kaufmann.
Kodratoff, Y., & Michalski, R. S. (1990). Machine learning: An artificial intelligence approach, (Vol. 3). San Mateo, CA: Morgan Kaufmann.
Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (1983). Machine learning: An artificial intelligence approach. Palo Alto, CA: Tioga Publishing Company.
Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (1986). Machine learning: An artificial intelligence approach, (Vol. 2). San Mateo, CA: Morgan Kaufmann.
Mitchell, T. M. (1997). Machine learning. Boston, MA: McGraw-Hill.
Russell, S., & Norvig, P. (2003). Artificial intelligence: A modern approach. Upper Saddle River, NJ: Prentice-Hall.
Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. San Fransisco: Morgan Kaufmann.
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(2011). Classification Algorithms. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_112
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