Supervised Neural Network Classification Algorithm: Difference between revisions
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=== 2006 === | === 2006 === | ||
* ([[2006_AnEmpiricalComparisonofSupervis|Caruana & Niculescu-Mizil, 2006]]) ⇒ [[Rich Caruana]], and [[Alexandru Niculescu-Mizil]]. ([[2006]]). “[http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf An Empirical Comparison of Supervised Learning Algorithms].” In: [[Proceedings of the 23rd International Conference on Machine learning]]. ISBN:1-59593-383-2 [http://dx.doi.org/10.1145/1143844.1143865 doi:10.1145/1143844.1143865] | * ([[2006_AnEmpiricalComparisonofSupervis|Caruana & Niculescu-Mizil, 2006]]) ⇒ [[Rich Caruana]], and [[Alexandru Niculescu-Mizil]]. ([[2006]]). “[http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf An Empirical Comparison of Supervised Learning Algorithms].” In: [[Proceedings of the 23rd International Conference on Machine learning]]. ISBN:1-59593-383-2 [http://dx.doi.org/10.1145/1143844.1143865 doi:10.1145/1143844.1143865] | ||
** QUOTE: [[2006_AnEmpiricalComparisonofSupervis|This paper]] presents results of a large-scale [[empirical system comparison|empirical comparison]] of ten [[supervised classification algorithm|supervised learning algorithm]]s using eight [[performance criteria]]. [[2006_AnEmpiricalComparisonofSupervis|We]] evaluate the performance of [[classification SVMs|SVMs]], [[Supervised Neural Network Classification Algorithm|neural nets]], [[logistic regression]], [[naive bayes]], [[memory-based learning]], [[random forests]], [[decision trees]], [[bagged trees]], [[boosted trees]], and [[boosted stumps]] on eleven [[Supervised Binary Classification Task|binary classification problem]]s using a variety of [[classification performance metric|performance metric]]s: [[accuracy]], [[F-score]], [[Lift]], [[ROC Area]], [[average precision]], [[precision/recall break-even point]], [[squared error]], and [[cross-entropy]]. | ** QUOTE: [[2006_AnEmpiricalComparisonofSupervis|This paper]] presents results of a large-scale [[empirical system comparison|empirical comparison]] of ten [[supervised classification algorithm|supervised learning algorithm]]s using eight [[performance criteria]]. [[2006_AnEmpiricalComparisonofSupervis|We]] evaluate the performance of [[classification SVMs|SVMs]], [[Supervised Neural Network Classification Algorithm|neural nets]], [[logistic regression]], [[naive bayes]], [[memory-based learning]], [[random forests]], [[decision trees]], [[bagged trees]], [[boosted trees]], and [[boosted stumps]] on eleven [[Supervised Binary Classification Task|binary classification problem]]s using a variety of [[classification performance metric|performance metric]]s: [[accuracy]], [[F-score]], [[Lift]], [[ROC Area]], [[average precision]], [[precision/recall break-even point]], [[squared error]], and [[cross-entropy]]. | ||
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Revision as of 14:10, 2 August 2022
A Supervised Neural Network Classification Algorithm is a Supervised Classification Algorithm that is a Neural Network Training Algorithm.
- …
- Counter-Example(s):
- See: Backprop.
References
2006
- (Caruana & Niculescu-Mizil, 2006) ⇒ Rich Caruana, and Alexandru Niculescu-Mizil. (2006). “An Empirical Comparison of Supervised Learning Algorithms.” In: Proceedings of the 23rd International Conference on Machine learning. ISBN:1-59593-383-2 doi:10.1145/1143844.1143865
- QUOTE: This paper presents results of a large-scale empirical comparison of ten supervised learning algorithms using eight performance criteria. We evaluate the performance of SVMs, neural nets, logistic regression, naive bayes, memory-based learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps on eleven binary classification problems using a variety of performance metrics: accuracy, F-score, Lift, ROC Area, average precision, precision/recall break-even point, squared error, and cross-entropy.