Add to ORKGWe show an algorithm that learns decision lists via equivalence queries, provided that a set G including all terms of the target list is given. The algorithm runs in time polynomial in the cardinality of G. From this last learning algorithm, we prove that log n-decision lists - the class of decision lists such that all their terms have low Kolmogorov complexity - are simple pac-learnable.Postprint (published version
We present a learning algorithm for decision lists which allows features that are constructed from t...
AbstractWe introduce a new representation class of Boolean functions – monotone term decision lists ...
Incremental learning algorithms are better suited for online learning tasks in principle. However, d...
We show an algorithm that learns decision lists via equivalence queries, provided that a set G inclu...
We prove that log n-decision lists - the class of decision lists such that all their terms have low ...
We study the learnability of monotone term decision lists in the exact model of equivalence and memb...
Abstractk-Decision lists and decision trees play important roles in learning theory as well as in pr...
We study the learnability of monotone term decision lists in the exact model of equivalence and memb...
We consider two well-studied problems regarding attribute efficient learning: learning decision lis...
We introduce a new representation class of Boolean functions---monotone term decision lists---which...
AbstractTwo fundamental measures of the efficiency of a learning algorithm are its running time and ...
We prove the following results. Any Boolean function of O(log n) relevant variables can be exactly l...
We introduce a new learning algorithm for decision lists to allow features that are constructed from...
AbstractThe paper introduces the notion of decision lists over regular patterns. This formalism prov...
This paper describes a new algorithm for learning decision lists that operates by prepending success...
We present a learning algorithm for decision lists which allows features that are constructed from t...
AbstractWe introduce a new representation class of Boolean functions – monotone term decision lists ...
Incremental learning algorithms are better suited for online learning tasks in principle. However, d...
We show an algorithm that learns decision lists via equivalence queries, provided that a set G inclu...
We prove that log n-decision lists - the class of decision lists such that all their terms have low ...
We study the learnability of monotone term decision lists in the exact model of equivalence and memb...
Abstractk-Decision lists and decision trees play important roles in learning theory as well as in pr...
We study the learnability of monotone term decision lists in the exact model of equivalence and memb...
We consider two well-studied problems regarding attribute efficient learning: learning decision lis...
We introduce a new representation class of Boolean functions---monotone term decision lists---which...
AbstractTwo fundamental measures of the efficiency of a learning algorithm are its running time and ...
We prove the following results. Any Boolean function of O(log n) relevant variables can be exactly l...
We introduce a new learning algorithm for decision lists to allow features that are constructed from...
AbstractThe paper introduces the notion of decision lists over regular patterns. This formalism prov...
This paper describes a new algorithm for learning decision lists that operates by prepending success...
We present a learning algorithm for decision lists which allows features that are constructed from t...
AbstractWe introduce a new representation class of Boolean functions – monotone term decision lists ...
Incremental learning algorithms are better suited for online learning tasks in principle. However, d...