It is well known that an algorithm that learns exactly using
Equivalence queries can be transformed into a PAC algorithm that asks for
random labelled examples.
The first transformation due to Angluin (1988) uses a number of
examples quadratic in the number of queries. Later, Littlestone (1989)
and Schuurmans and Greiner (1995) gave transformations
using linearly many examples. We present here another analysis of
Littlestone's transformation which is both simpler and
gives better leading constants.
Our constants are still worse than Schuurmans and
Greiner's, but while ours
is a worst-case bound on the number of examples to achieve PAC
learning, theirs is only an expected one.
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder. If you wish to make any use of the work not provided for in the law, please contact: firstname.lastname@example.org