Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
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
Polynomial Time Inductive Inference of Regular Term Tree Languages from Positive Data
ALT '97 Proceedings of the 8th International Conference on Algorithmic 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
A polynomial time algorithm for finding linear interval graph patterns
TAMC'07 Proceedings of the 4th international conference on Theory and applications of models of computation
Polynomial time inductive inference of TTSP graph languages from positive data
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Learning Block-Preserving Outerplanar Graph Patterns and Its Application to Data Mining
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
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A linear graph pattern is a labeled graph such that its vertices have constant labels and its edges have either constant or mutually distinct variable labels. An edge having a variable label is called a variable and can be replaced with an arbitrary labeled graph. Let ${\mathcal GPC}$ be the set of all linear graph patterns having a structural feature ${\mathcal C}$ like "having a tree structure", "having a two-terminal series parallel graph structure" and so on. The graph language GLc(g) of a linear graph pattern gin ${\cal GP}({\mathcal C})$ is the set of all labeled graphs obtained from gby substituting arbitrary labeled graphs having the structural feature ${\mathcal C}$ to all variables in g. In this paper, for any set ${\cal T_*}$ of mlinear graph patterns in ${\cal GP}({\mathcal C})$, we present a query learning algorithm for finding a set Sof linear graph patterns in ${\cal GP}({\mathcal C})$ with $\bigcup_{g\in{\cal T_*}}GLc{(g)}=\bigcup_{f\in S}GLc{(f)}$ in polynomial time using at most m+ 1 equivalence queries and O(m(n+ n2)) restricted subset queries, where nis the maximum number of edges of counterexamples, if the number of labels of edges is infinite. Next we show that finite sets of graph languages generated by linear graph patterns having tree structures or two-terminal series parallel graph structures are not learnable in polynomial time using restricted equivalence, membership and subset queries.