Greedy Lazy Model-based Classification Algorithm: Difference between revisions
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=== 2006 === | === 2006 === | ||
* ([[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 Classification Algorithm|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 Classification Algorithm|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 Greedy Lazy Model-based Classification Algorithm is a Lazy Model-based Classification Algorithm that is a Greedy Model-based Classification Algorithm.
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.