Learning Algorithms for Grammars of Variable Arity Trees
N. Sebastian,K. Krithivasan +1 more
- pp 98-103
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TLDR
This paper gives algorithms for inference of local, single type and regular grammar and also considers the use of negative samples in the inference of tree grammars from a set of sample input trees.Abstract:
Grammatical Inference is the technique by which a grammar that best describes a given set of input samples is inferred. This paper considers the inference of tree grammars from a set of sample input trees. Inference of grammars for fixed arity trees is well studied, in this paper we extend the method to give algorithms for inference of grammars for variable arity trees. We give algorithms for inference of local, single type and regular grammar and also consider the use of negative samples. The variable arity trees we consider can be used for representation of XML documents and the algorithms we have given can be used for validation as well as for schema inference.read more
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References
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Using AUC and accuracy in evaluating learning algorithms
Jin Huang,Charles X. Ling +1 more
TL;DR: It is shown theoretically and empirically that AUC is a better measure (defined precisely) than accuracy and reevaluate well-established claims in machine learning based on accuracy using AUC and obtain interesting and surprising new results.
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Robust Classification for Imprecise Environments
Foster Provost,Tom Fawcett +1 more
TL;DR: It is shown that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions, and in some cases, the performance of the hybrid actually can surpass that of the best known classifier.
Posted Content
Robust Classification for Imprecise Environments
Foster Provost,Tom Fawcett +1 more
TL;DR: The ROC convex hull (ROCCH) method as mentioned in this paper combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers.