Learning unions of tree patterns using queries
Theoretical Computer Science - Special issue on algorithmic learning theory
Exact learning of tree patterns from queries and counterexamples
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning subsequence languages
Information modelling and knowledge bases VIII
On Exact Learning of Unordered Tree Patterns
Machine Learning
Machine Learning
Machine Learning
Learning of Finite Unions of Tree Patterns with Internal Structured Variables from Queries
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Discovery of Frequent Tag Tree Patterns in Semistructured Web Documents
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Learning Pattern Languages Using Queries
EuroCOLT '97 Proceedings of the Third European Conference on Computational Learning Theory
Efficient Learning of Semi-structured Data from Queries
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
A Polynomial Time Algorithm for Finding Finite Unions of Tree Pattern Languages
Proceedings of the Second International Workshop on Nonmonotonic and Inductive Logic
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Ordered term tree languages which are polynomial time inductively inferable from positive data
Theoretical Computer Science - Algorithmic learning theory(ALT 2002)
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
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A linear term tree is defined as an edge-labeled rooted tree pattern with ordered children and internal structured variables whose labels are mutually distinct. A variable can be replaced with arbitrary edge-labeled rooted ordered trees. We consider the polynomial time learnability of finite unions of linear term trees in the exact learning model formalized by Angluin. The language L(t) of a linear term tree t is the set of all trees obtained from t by substituting arbitrary edge-labeled rooted ordered trees for all variables in t. Moreover, for a finite set S of linear term trees, we define L(S) = ∪t∈SL(t). A target of learning, denoted by T*, is a finite set of linear term trees, where the number of edge labels is infinite. In this paper, for any set T* of m linear term trees (m ≥ 0), we present a query learning algorithm which exactly identifies T* in polynomial time using at most 2mn2Restricted Subset queries and at most m + 1 Equivalence queries, where n is the maximum size of counterexamples. Finally, we note that finite sets of linear term trees are not learnable in polynomial time using Restricted Equivalence, Membership and Subset queries.