Lazy Model-based Supervised Classification Algorithm: Difference between revisions
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* ([[Malyshkin et al., 2006]]) ⇒ [[Vladislav Malyshkin]], [[Ray Bakhramov]], [[Andrey Gorodetsky]]. ([[2006]]). “[http://arxiv.org/abs/cs/0609007 A Massive Local Rules Search Approach to the Classification Problem].” In: [[ArXiV]] | * ([[Malyshkin et al., 2006]]) ⇒ [[Vladislav Malyshkin]], [[Ray Bakhramov]], [[Andrey Gorodetsky]]. ([[2006]]). “[http://arxiv.org/abs/cs/0609007 A Massive Local Rules Search Approach to the Classification Problem].” In: [[ArXiV]]. | ||
** QUOTE: … An interesting attempt to combine model based and lazy instance based learning was presented in ([[1998_LazyModelBasedOnlineClassification|Melli, 1998]]). In ([[1998_LazyModelBasedOnlineClassification|Melli, 1998]]) a [[greedy lazy model–based approach for classification]] was developed in which the result was a rule tailored to the specific observation. While such an approach gives a simple rule as an answer (which is often much easier to understand than a complex rules set) and often works faster for classification of a single event, it–as every greedy algorithm–is not guaranteed to find the best rule, because [[the algorithm]] may not reach the global maximum of the quality criterion and a sub–optimal rule may be returned. | ** QUOTE: … An interesting attempt to combine model based and lazy instance based learning was presented in ([[1998_LazyModelBasedOnlineClassification|Melli, 1998]]). In ([[1998_LazyModelBasedOnlineClassification|Melli, 1998]]) a [[greedy lazy model–based approach for classification]] was developed in which the result was a rule tailored to the specific observation. While such an approach gives a simple rule as an answer (which is often much easier to understand than a complex rules set) and often works faster for classification of a single event, it–as every greedy algorithm–is not guaranteed to find the best rule, because [[the algorithm]] may not reach the global maximum of the quality criterion and a sub–optimal rule may be returned. | ||
Latest revision as of 13:55, 6 July 2022
A Lazy Model-based Supervised Classification Algorithm is a lazy classification algorithm that is a model-based classification algorithm.
- Example(s):
- Counter-Example(s):
- See: Lazy Model-based Learning.
References
2006
- (Malyshkin et al., 2006) ⇒ Vladislav Malyshkin, Ray Bakhramov, Andrey Gorodetsky. (2006). “A Massive Local Rules Search Approach to the Classification Problem.” In: ArXiV.
- QUOTE: … An interesting attempt to combine model based and lazy instance based learning was presented in (Melli, 1998). In (Melli, 1998) a greedy lazy model–based approach for classification was developed in which the result was a rule tailored to the specific observation. While such an approach gives a simple rule as an answer (which is often much easier to understand than a complex rules set) and often works faster for classification of a single event, it–as every greedy algorithm–is not guaranteed to find the best rule, because the algorithm may not reach the global maximum of the quality criterion and a sub–optimal rule may be returned.
1998
- (Melli, 1998) ⇒ Gabor Melli. (1998). “A Lazy Model-based Approach to On-Line Classification." Master's Thesis, Simon Fraser University.
1996
- (Melli, 1996) ⇒ Gabor Melli. (1996). “Ad Hoc Attribute-Value Prediction.” In: Proceedings of AAAI 1996. (AAAI 1996).
- (Friedman et al., 1996) ⇒ Jerome H. Friedman, Ron Kohavi, and Yeogirl Yun. (1996). “Lazy Decision Trees.” In: Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI 1996).