Learning Multiplicity Tree Automata
Amauri Habrard and Jose Oncina
Lecture Notes in Computer Science
In this paper, we present a theoretical approach for the problem
of learning multiplicity tree automata.
These automata allows one to define functions which compute
a number for each tree. They can be seen as a strict
generalization of stochastic tree automata since they allow to
define functions over any field K.
A multiplicity automaton admits a support which is a non deterministic
From a grammatical inference point of view,
this paper presents a contribution which is original due to
the combination of two important aspects. This is the first time,
as far as we now,
that a learning method focuses on non deterministic tree automata
which computes functions over a field.
The algorithm proposed in this paper stands in Angluin's
exact model where a learner is allowed to use membership
and equivalence queries.
We show that this algorithm is polynomial in time in function of the
size of the representation.