Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Classification Algorithms

Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_112

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

  1. Bishop, C. M. (2007). Pattern recognition and machine learning. New York: Springer.Google Scholar
  2. Dietterich, T., & Shavlik, J. (Eds.). Readings in machine learning. San Mateo, CA: Morgan Kaufmann.Google Scholar
  3. Kodratoff, Y., & Michalski, R. S. (1990). Machine learning: An artificial intelligence approach, (Vol. 3). San Mateo, CA: Morgan Kaufmann.Google Scholar
  4. Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (1983). Machine learning: An artificial intelligence approach. Palo Alto, CA: Tioga Publishing Company.Google Scholar
  5. Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (1986). Machine learning: An artificial intelligence approach, (Vol. 2). San Mateo, CA: Morgan Kaufmann.Google Scholar
  6. Mitchell, T. M. (1997). Machine learning. Boston, MA: McGraw-Hill.MATHGoogle Scholar
  7. Russell, S., & Norvig, P. (2003). Artificial intelligence: A modern approach. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
  8. Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. San Fransisco: Morgan Kaufmann.MATHGoogle Scholar

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