By K. Kersting

ISBN-10: 1429455276

ISBN-13: 9781429455275

ISBN-10: 1586036742

ISBN-13: 9781586036744

During this booklet, the writer Kristian Kersting has made an attack on one of many toughest integration difficulties on the middle of man-made Intelligence study. This consists of taking 3 disparate significant components of study and making an attempt a fusion between them. the 3 parts are: common sense Programming, Uncertainty Reasoning and computing device studying. almost all these is a massive sub-area of analysis with its personal linked overseas learn meetings. Having taken on this type of Herculean job, Kersting has produced a sequence of effects that are now on the center of a newly rising quarter: Probabilistic Inductive common sense Programming. the hot zone is heavily tied to, even though strictly subsumes, a brand new box often called 'Statistical Relational studying' which has within the previous few years won significant prominence within the American man made Intelligence study group. inside of this booklet, the writer makes numerous significant contributions, together with the advent of a chain of definitions which circumscribe the recent zone shaped by means of extending Inductive good judgment Programming to the case within 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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**Extra resources for An Inductive Logic Programming Approach to Statistical Relational Learning**

**Sample text**

The underlying logic program of the hypothesis, the parameters, or both. To come up with algorithms solving probabilistic ILP learning problems, say for density estimation, one typically distinguishes two subtasks because H = (L, λ) is essentially a logic program L annotated with probabilistic parameters λ: (1) Parameter estimation where it is assumed that the underlying logic program L is ﬁxed, and the learning task consists of estimating the parameters λ that maximize the likelihood. (2) Structure learning where both L and λ have to be learned from the data.

Even though this is generally true, there exist speciﬁc situations for which this is feasible. , 1994], which contain parse trees. These trees directly correspond to the proof-trees we talk about. Another example is explanation-based learning (EBL) [Ellman, 1989, Mooney and Zelle, 1994]. , generalizes the proof as far as possible while maintaining it’s correctness (generalization step). 4 Inductive Logic Programming Techniques Given the diﬀerent learning settings, there are — broadly speaking — three types of ILP approaches.

Then the least Herbrand model of P is the Im with smallest m ≥ 0 such that Im+1 = Im . In case of functor-free, range-restricted clauses, the least Herbrand model can be obtained using the following procedure: 1: Initialize LH := ∅ 2: repeat 3: LH := TP (LH) 4: until LH does not change anymore That is, initialize LH to the empty set, and then add all ground facts head(c)θ to LH for which there exists a clause c ∈ P and a substitution such that body(c)θ ⊆ LH. Such ground facts are called immediate consequences of body(c)θ.

### An Inductive Logic Programming Approach to Statistical Relational Learning by K. Kersting

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