Titlebook: Inductive Logic Programming; 21st International C Stephen H. Muggleton,Alireza Tamaddoni-Nezhad,Fran Conference proceedings 2012 Springer-V

analogous

Subgroup Discovery Using Bump Hunting on Multi-relational Histogramsograms constructed from substitution sets resulting from matching a first-order query against the input relational database. The approach is evaluated on seven data sets, discovering interpretable subgroups. The subgroups’ rate of survival from the training split to the testing split varies among th

Cervical-Spine

鸵鸟

Negotiate

habitat

Predictive Sequence Miner in ILP Learnings main idea consists of exploiting frequent sequence mining, an efficient method to learn temporal patterns in the form of sequences. . framework efficiency is grounded on a new coding methodology for temporal data and on the use of a predictive sequence miner. The frameworks selects and map the mos

不妥协

Conceptual Clustering of Multi-Relational Data” to objects belonging to the other groups. In contrast, in conceptual clustering the underlying structure of the data together with the description language which is available to the learner is what drives cluster formation, thus providing intelligible descriptions of the clusters, facilitating the

不舒服

DNF Hypotheses in Explanatory Inductionl setting of ILP, where hypotheses are obtained in conjunctive normal form (CNF), i.e., a set of clauses. We present two approaches to compute DNF hypotheses as well as several sound and complete algorithms. This problem naturally contains abduction from clausal theories, and can be related to model

vitrectomy

Variational Bayes Inference for Logic-Based Probabilistic Models on BDDsal abduction attempts to define a probability distribution over explanations and to evaluate them by their probabilities. . (LBPMs) have been developed as a way to combine probabilities and logic, and it enables us to perform statistical abduction. However non-deterministic knowledge like preference

外形

Inference and Learning in Planninghods for deriving generalized policies and finite state controllers capable of dealing with changes in the initial situation and in the number of objects. I’ll also discuss the alternative ways in which learning can be used in planning and the challenges ahead.

苍白

Learning Directed Relational Models with Recursive Dependenciesin algorithm of Khosravi ., which is a state-of-the art structure learning algorithm that upgrades propositional Bayes net learners for relational data. Emprical evaluation compares our approach to learning recursive dependencies with undirected models (Markov Logic Networks). The Bayes net approach