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In common usage, the word classification means to put things into categories, group them together in some useful way. If we are screening for a disease, we would group people into those with the disease and those without. We, as humans, usually do this because things in a group, called a class in machine learning, share common characteristics. If we know the class of something, we know a lot about it. In machine learning, the term classification is most commonly associated with a particular type of learning where examples of one or more classes, labeled with the name of the class, are given to the learning algorithm. The algorithm produces a classifier which maps the properties of these examples, normally expressed as attribute-value pairs, to the class labels. A new example whose class is unknown is classified when it is given a class label by the classifier based on its properties. In machine...
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Aha DW (1997) Editorial. Artif Intell Rev 11(1–5):1–6
Aha DW, Riddle PJ (eds)(1995) Workshop on applying machine learning in practice. In: Proceedings of the 12th international conference on machine learning, Tahoe City
Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6(1):37–66
Ashby FG, Maddox WT (2005) Human category learning. Ann Rev Psychol 56:149–178
Bishop CM (2007) Pattern recognition and machine learning. Springer, New York
Brachman RJ, Khabaza T, Kloesgen W, Piatetsky-Shapiro G, Simoudis E (1996) Mining business databases. Commun ACM 39(11):42–48
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont
Caruana R, Niculescu-Mizil A, Crew G, Ksikes A (2004) Ensemble selection from libraries of models. In: Proceedings of the 21st international conference on machine learning, Banff, pp 137–144
Clark P, Niblett T (1989) The CN2 induction algorithm. Mach Learn 3:261–284
Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21–27
Dietterich T, Shavlik J (eds) Readings in machine learning. Morgan Kaufmann, San Mateo
Engels R, Evans B, Herrmann J, Verdenius F (eds) (1997) Workshop on machine learning applications in the real world; methodological aspects and implications. In: Proceedings of the 14th international conference on machine learning, Nashville
Fayyad UM, Uthurusamy R (eds)(1995) Proceedings of the first international conference on knowledge discovery and data mining, Montreal
Holte RC (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn 11(1):63–91
Kodratoff Y (ed)(1994) Proceedings of MLNet workshop on industrial application of machine learning, Douran
Kodratoff Y, Michalski RS (1990) Machine learning: an artificial intelligence approach, vol 3. Morgan Kaufmann, San Mateo
Kohavi R, Provost F (1998) Glossary of terms. Editorial for the special issue on applications of machine learning and the knowledge discovery process. Mach Learn 30(2/3)
Komorowski HJ, Zytkow JM (eds) (1997) Proceedings of the first European conference on principles of data mining and knowledge discovery
Lakoff G (1987) Women, fire and dangerous things. University of Chicago Press, Chicago
Langley P, Simon HA (1995) Applications of machine learning and rule induction. Commun ACM 38(11):54–64
Michalski RS (1983) A theory and methodology of inductive learning. In: Michalski RS, Carbonell TJ, Mitchell TM (eds) Machine learning: an artificial intelligence approach. TIOGA Publishing, Palo Alto, pp 83–134
Michalski RS, Carbonell JG, Mitchell TM (eds) (1983) Machine learning: an artificial intelligence approach. Tioga Publishing Company, Palo Alto
Michalski RS, Carbonell JG, Mitchell TM (eds) (1986) Machine learning: an artificial intelligence approach, vol 2. Morgan Kaufmann, San Mateo
Michie D (1982) Machine intelligence and related topics. Gordon and Breach Science Publishers, New York
Mitchell TM (1977) Version spaces: a candidate elimination approach to rule learning. In: Proceedings of the fifth international joint conferences on artificial intelligence, Cambridge, pp 305–310
Mitchell TM (1997) Machine learning. McGraw-Hill, Boston
Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106
Quinlan JR (1993) C4.5 programs for machine learning. Morgan Kaufmann, San Mateo
Rubinstein YD, Hastie T (1997) Discriminative vs informative learning. In: Proceedings of the third international conference on knowledge discovery and data mining, Newport Beach, pp 49–53
Russell S, Norvig P (2003) Artificial intelligence: a modern approach. Prentice-Hall, Upper Saddle River
Schorr H, Rappaport A (eds) (1989) Proceedings of the first conference on innovative applications of artificial intelligence, Stanford
Winston PH (1975) Learning structural descriptions from examples. In: Winston PH (ed) The psychology of computer vision. McGraw-Hill, New York, pp 157–209
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, San Fransisco
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82
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Drummond, C. (2017). Classification. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_111
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