An Inductive Logic Programming Approach to Statistical by K. Kersting

An Inductive Logic Programming Approach to Statistical by K. Kersting

By K. Kersting

During this booklet, the writer Kristian Kersting has made an attack on one of many toughest integration difficulties on the center of synthetic Intelligence examine. This includes taking 3 disparate significant components of study and trying a fusion between them. the 3 components are: good judgment Programming, Uncertainty Reasoning and computing device studying. most of these is an enormous sub-area of study with its personal linked overseas study meetings. Having taken on this kind of Herculean activity, Kersting has produced a chain of effects that are now on the middle of a newly rising sector: Probabilistic Inductive common sense Programming. the hot region is heavily tied to, even though strictly subsumes, a brand new box referred to as 'Statistical Relational studying' which has within the previous few years received significant prominence within the American man made Intelligence learn neighborhood. inside this ebook, the writer makes a number of significant contributions, together with the creation of a chain of definitions which circumscribe the hot zone shaped via extending Inductive good judgment Programming to the case during which clauses are annotated with likelihood values. additionally, Kersting investigates the procedure of studying from proofs and the difficulty of upgrading Fisher Kernels to Relational Fisher Kernels.

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Father(brian, dorothy). father(brian, rex). From this information, we could induce H daughter(C, P) : − female(C), mother(P, C). daughter(C, P) : − female(C), father(P, C). , Pos are entailed by H together with B, but Neg are not entailed. ◦ More formally, ILP is concerned with the following learning problem. 5 (ILP Learning Problem) Given a set of positive and negative examples Pos and Neg over some language LE , a background theory B, in the form of a set of definite clauses, a hypothesis language LH , which specifies the clauses that are allowed in hypotheses, and a covers relation covers(e, H, B) ∈ {0, 1}, which basically returns the classification of an example e with respect to H and B, find a hypothesis H in H that covers (with respect to the background theory B) all positive examples in Pos (completeness) and none of the negative examples in Neg (consistency).

In the remainder of the thesis, we will then discuss selected probabilistic ILP approaches for learning from interpretations and probabilistic learning from traces in detail. A more complete survey of learning probabilistic logic representations can be found in [De Raedt and Kersting, 2003] and in the related work sections of this thesis. 1 Parameter Estimation The problem of parameter estimation is thus concerned with estimating the values of the parameters λ of a fixed probabilistic program H = (L, λ) that best explains the examples E.

1/2 : np(Number, A, B) ← det(A, C), n(Number, C, B). 1/2 : np(Number, A, B) ← pronom(Number, A, B). 1/2 : vp(Number, A, B) ← v(Number, A, B). 1/2 : vp(Number, A, B) ← v(Number, A, C), np(D, C, B). 1 : det(A, B) ← term(A, the, B). 1/4 : n(s, A, B) ← term(A, man, B). 1/4 : n(s, A, B) ← term(A, apple, B). 1/4 : n(pl, A, B) ← term(A, men, B). 1/4 : n(pl, A, B) ← term(A, apples, B). 1/4 : v(s, A, B) ← term(A, eats, B). 1/4 : v(s, A, B) ← term(A, sings, B). 1/4 : v(pl, A, B) ← term(A, eat, B). 1/4 : v(pl, A, B) ← term(A, sing, B).

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