Titlebook: Inductive Logic Programming; 20th International C Paolo Frasconi,Francesca A. Lisi Conference proceedings 2011 The Editor(s) (if applicable

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书目名称Inductive Logic Programming影响因子(影响力)




书目名称Inductive Logic Programming影响因子(影响力)学科排名




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书目名称Inductive Logic Programming网络公开度学科排名




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书目名称Inductive Logic Programming被引频次学科排名




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书目名称Inductive Logic Programming年度引用学科排名




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书目名称Inductive Logic Programming读者反馈学科排名

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Learning Multi-class Theories in ILPhnique when dealing with multi-class domains. We show that we can learn a simple, consistent and reliable multi-class theory by combining the rules of the multiple one-vs-rest theories into one rule list or set. We experimentally show that our proposed methods produce coherent and accurate rule mode

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A Numerical Refinement Operator Based on Multi-Instance Learningis delegated to statistical multi-instance learning schemes. To each clause, there is an associated multi-instance classification model with the numerical variables of the clause as input. Clauses are built in a greedy manner, where each refinement adds new numerical variables which are used additio

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Not Far Away from Home: A Relational Distance-Based Approach to Understanding Images of Housesation. In this paper we tackle the problem of delimiting conceptual elements of street views based on . between lower-level components, e.g. the element ‘house’ is composed of windows and a door in a spatial arrangement. We use structured data: each concept can be seen as a graph representing spatia

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Approximate Bayesian Computation for the Parameters of PRISM Programs learning hard, even when structure is fixed and learning reduces to parameter estimation. In this paper an approximate Bayesian computation (ABC) method is presented which computes approximations to the posterior distribution over PRISM parameters. The key to ABC approaches is that the likelihood f

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