By K. Kersting
ISBN-10: 1429455276
ISBN-13: 9781429455275
ISBN-10: 1586036742
ISBN-13: 9781586036744
During this book, the writer Kristian Kersting has made an attack on one of many toughest integration difficulties on the center of synthetic Intelligence learn. This consists of taking 3 disparate significant parts of study and trying a fusion between them. the 3 parts are: common sense Programming, Uncertainty Reasoning and computer studying. most of these is a huge sub-area of analysis with its personal linked overseas examine meetings. Having taken on one of these Herculean job, Kersting has produced a sequence of effects that are now on the center of a newly rising region: Probabilistic Inductive common sense Programming. the hot zone is heavily tied to, notwithstanding strictly subsumes, a brand new box often called 'Statistical Relational studying' which has within the previous few years received significant prominence within the American man made Intelligence examine group. inside of this ebook, the writer makes a number of significant contributions, together with the advent of a chain of definitions which circumscribe the hot zone shaped through extending Inductive good judgment Programming to the case during which clauses are annotated with chance values. additionally, Kersting investigates the process of studying from proofs and the problem of upgrading Fisher Kernels to Relational Fisher Kernels.
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Additional resources for An Inductive Logic Programming Approach to Statistical Relational Learning
Sample text
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).
143). 013). 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.
This is basically done by deleting literals (or clauses), by turning constants into variables and/or bounded variables into new variables. Reconsider for instance the learning from proofs setting. By analogy with the learning of tree-bank grammars, one could turn all the proof-trees (corresponding to positive examples) into a set of ground clauses, which would constitute the initial theory. This theory can then be generalized by taking the least general generalization (under θ-subsumption) of pairwise clauses.
An Inductive Logic Programming Approach to Statistical Relational Learning by K. Kersting
by Robert
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