Learning decision lists
نویسندگان
چکیده
منابع مشابه
Learning Monotone Term Decision Lists
We study the learnability of monotone term decision lists in the exact model of equivalence and membership queries. We show that, for any constant k 0, k-term monotone decision lists are exactly and properly learnable with n O(k) membership queries in O(n k 3) time. We also show n (k) membership queries are necessary for exact learning. In contrast, both k-term monotone decision lists (k 2) and...
متن کاملLearning Decision Lists U sing
A decision list is an ordered list of conjunctive rules (Rivest 1987). Inductive algorithms such as AQ and CN2 learn decision lists incrementally, one rule at a time. Such algorithms face the rule overlap problem -the classification accuracy of the decision list depends on the overlap between the learned rules. Thus, even though the rules are learned in isolation, they can only be evaluated in ...
متن کاملLearning Decision Lists Using Homogeneous
A decision list is an ordered list of conjunctive rules (Rivest 1987). Inductive algorithms such as AQ and CN2 learn decision lists incrementally, one rule at a time. Such algorithms face the rule overlap problem the classification accuracy of the decision list depends on the overlap between the learned rules. Thus, even though the rules are learned in isolation, they can only be evaluated in c...
متن کاملOn Online Learning of Decision Lists
A fundamental open problem in computational learning theory is whether there is an attribute efficient learning algorithm for the concept class of decision lists (Rivest, 1987; Blum, 1996). We consider a weaker problem, where the concept class is restricted to decision lists with D alternations. For this class, we present a novel online algorithm that achieves a mistake bound of O(r log n), whe...
متن کاملLearning Decision Lists by Prepending Inferred Rules
This paper describes a new algorithm for learning decision lists that operates by prepending successive rules to front of the list under construction. This contrasts with the original decision list induction algorithm which operates by appending successive rules to end of the list under construction.. The new algorithm is demonstrated in the majority of cases to produce smaller classifiers that...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 1987
ISSN: 0885-6125,1573-0565
DOI: 10.1007/bf00058680