Gerson Zaverucha, Vítor Santos Costa, Aline Paes's Inductive Logic Programming: 23rd International Conference, PDF

By Gerson Zaverucha, Vítor Santos Costa, Aline Paes

ISBN-10: 3662449226

ISBN-13: 9783662449226

ISBN-10: 3662449234

ISBN-13: 9783662449233

This e-book constitutes the completely refereed post-proceedings of the twenty third overseas convention on Inductive good judgment Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013.
The nine revised prolonged papers have been rigorously reviewed and chosen from forty two submissions. The convention now makes a speciality of all features of studying in common sense, multi-relational studying and information mining, statistical relational studying, graph and tree mining, relational reinforcement studying, and other kinds of studying from based data.

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This ebook constitutes the completely refereed post-proceedings of the twenty third foreign convention on Inductive good judgment Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013. The nine revised prolonged papers have been rigorously reviewed and chosen from forty two submissions. The convention now makes a speciality of all features of studying in common sense, multi-relational studying and information mining, statistical relational studying, graph and tree mining, relational reinforcement studying, and other kinds of studying from dependent information.

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Additional resources for Inductive Logic Programming: 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers

Example text

That is, if an ILP algorithm can be thought of as a sequence of count queries, and if the number of count queries exceeds a certain threshold, then the ILP algorithm cannot produce a result of high quality. This is a discouraging result, which states that in general, it is very hard to simultaneously guarantee both differential privacy and a high utility requirement since L2 grows exponentially with the number of atoms. Theorem 3 suggests that in order to decrease the lower bound on the privacy parameter, we must either increase the size of the database D , or reduce the number of atoms in the hypotheses space L2 .

The latter scheme, given by the number of positive examples covered minus the number of negative examples covered and the hypothesis size would be unsuitable as it would give too much emphasis to the hypothesis size, which is not relevant for discriminating among potential revisions that have to lead to consistent and complete hypothesis. e. number of positive examples minus number of negative coverage) over the hypothesis size. It has the advantage of having smaller range of score values (from −|E − | to |E + | as opposed to 0 to |E + | × |E − |), which could help reduce the search for the optimal hypothesis.

More precisely, for any database τ ∈ D, let nbrs(τ ) denote the set of neighboring databases of τ , each of which differs from τ by at most one row. Differential privacy requires that the probability of an algorithm to output the same result on any pair of neighboring databases are bounded by a constant ratio. Definition 2 ( -differential privacy [2]): For any input database τ , a randomized algorithm f is -differentially private iff for any S ⊆ Range(f ), and any database τ ∈ nbrs(τ ), Pr(f (τ ) ∈ S) ≤ e Pr(f (τ ) ∈ S) where Pr is the probability taken over the coin tosses of the algorithm f .

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Inductive Logic Programming: 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers by Gerson Zaverucha, Vítor Santos Costa, Aline Paes


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