By Gerson Zaverucha, Vítor Santos Costa, Aline Paes

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

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

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A (revised and partial) hypothesis that covers the highest number of positive examples and the least number of negative examples, with it, or for change transaction its corresponding revised partial hypothesis, being the most concise. To compare hypotheses, we define a notion of score of a (partial) hypothesis, which induces a total ordering over partial hypotheses. Definition 2. Let E, B, M be an ILP task and let H be a partial hypothesis in RM . The score of H is the tuple score(H) = covere+ (H), covere− (H), len(H) , where covere+ (H) (resp.

D from the geometric distribution G( /Sq ) (1), guarantees -differential privacy for q. As proved in [6], a sequence of differentially private computations also ensures differential privacy. This is called the composition property of differential privacy as shown in Theorem 2. Theorem 2. [6] Given a sequence of computations, denoted as f = f1 ,. ,fd , i=d if each computation fi guarantees i -differential privacy, then f is ( i=1 i )differentially private. 3 Problem Formulation In analogy to Definition 2, we formulate the problem of guaranteeing differential privacy to ILP in Definition 4.

Line 3 or line 9) the induction hypothesis can be applied since Imax − (i + 1) < Imax − i. Case 3: (Imax − i > 0 and Learn is not called recursively). e. score can only increase a finite number of times). For RefineHypothesis to be repeatedly called, each time the score of the revised hypothesis has to be better than the current hypothesis. But the score can get better by either the number of positive example covered increases, or the number of negative examples covered decreases. This can continue until eventually a refined consistent and complete hypothesis is reached with score |E + |, 0, K , where K ≥ L.

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