Research note on decision lists
نویسندگان
چکیده
منابع مشابه
On 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 کاملOn the Isomorphism Problem for Decision Trees and Decision Lists
We study the complexity of isomorphism testing for boolean functions that are represented by decision trees or decision lists. Our results are the following: • Isomorphism testing of rank 1 decision trees is complete for logspace. • For any constant r ≥ 2, isomorphism testing for rank r decision trees is polynomial-time equivalent to Graph Isomorphism. As a consequence of our reduction, we obta...
متن کاملMonotone term decision lists
We introduce a new representation class of Boolean functions|monotone term decision lists|which combines compact representation size with tractability of essential operations. We present many properties of the class which make it an attractive alternative to traditional universal representation classes such as DNF formulas or decision trees. We study the learnability of monotone term decision l...
متن کاملDecision Lists for Lexical Ambiguityresolution
This paper presents a statistical decision procedure for lexical ambiguity resolution. The algorithm exploits both local syntactic patterns and more distant collo-cational evidence, generating an eecient, eeective, and highly perspicuous recipe for resolving a given ambiguity. By identifying and utilizing only the single best dis-ambiguating evidence in a target context, the algorithm avoids th...
متن کاملPruning Decision Trees and Lists
Machine learning algorithms are techniques that automatically build models describing the structure at the heart of a set of data. Ideally, such models can be used to predict properties of future data points and people can use them to analyze the domain from which the data originates. Decision trees and lists are potentially powerful predictors and embody an explicit representation of the struc...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 1993
ISSN: 0885-6125,1573-0565
DOI: 10.1007/bf00993105