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 middle of synthetic Intelligence examine. This consists of taking 3 disparate significant components of analysis and making an attempt a fusion between them. the 3 components are: good judgment Programming, Uncertainty Reasoning and computer studying. most of these is an enormous sub-area of study with its personal linked foreign learn meetings. Having taken on this type of Herculean activity, Kersting has produced a chain of effects that are now on the center of a newly rising region: Probabilistic Inductive common sense Programming. the recent zone is heavily tied to, although strictly subsumes, a brand new box referred to as 'Statistical Relational studying' which has within the previous couple of years won significant prominence within the American synthetic Intelligence learn neighborhood. inside this publication, the writer makes numerous significant contributions, together with the advent of a chain of definitions which circumscribe the hot quarter shaped via extending Inductive good judgment Programming to the case within which clauses are annotated with chance values. additionally, Kersting investigates the strategy 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

Example text

043). bond(225, f1 ... ring size 5(225, [f1 5, f1 1, f1 2, f1 3, f1 4]). hetero aromatic 5 ring(225, [f1 5, f1 1, f1 2, f1 3, f1 4]). 1, f1 2, 7). 2, f1 3, 7). 3, f1 4, 7). 4, f1 5, 7). 5, f1 1, 7). 8, f1 9, 2). 8, f1 10, 2). 1, f1 11, 1). 11, f1 12, 2). 11, f1 13, 1). Consider now the positive example mutagenic(225). It is covered by H mutagenic(M) : − nitro(M, R1), logp(M, C), C > 1. together with the background knowledge B, because H ∪ B entails the example. To see this, we unify mutagenic(225) with the clause’s head.

A clause c1 θ-subsumes 3 a clause c2 , denoted as c2 θ c1 , if and only if {head(c2 )θ} ∪ body(c2 )θ ⊂ {head(c1 )} ∪ body(c1 ). For instance, p(X) : − q(X) subsumes p(X) : − q(X), r(Y). θ-subsumption is reflexive and transitive, but not antisymmetric as p(X) : − q(X) and p(X) : − q(X), q(Y) show. , a partially ordered set of equivalence classes. We say that a clause is reduced if it does not θ-subsume any of its subclauses. Every equivalence class contains a reduced clause that is unique up to variable renaming.

ILP systems that learn from interpretations 18 §2 Probabilistic Inductive Logic Programming work in a similar fashion as those that learn from entailment. 15: When learning from entailment, G is more general than S if and only if G |= S, whereas when learning from interpretations, when S |= G. Another difference is that learning from interpretations is well suited for learning from positive examples only. For this case, a complete search of the space ordered by θ-subsumption is performed until all clauses cover all examples [De Raedt and Dehaspe, 1997].

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