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Showing 1 to 11 of 11 for “"Inductive logic programming"”.
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Inductive logic programming with gradient descent for supervised binary classification
… seeks to develop an interpretable model using logical rules, rather than explaining existing blackbox models. We extend recent inductive logic programming methods developed by Evans and Grefenstette [3] to develop an gradient descent-based inductive logic programming technique for supervised …
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An inductive logic programming approach to learning which uORFs regulate gene expression.
… In this thesis, for the first time, the use of inductive logic programming (ILP) is explored for the task of learning which uORFs regulate gene expression in the yeast Saccharomyces cerevisiae. This work is directed to help select sets of candidate functional uORFs for experimental studies. With …
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A sequence-length sensitive approach to learning biological grammars using inductive logic programming.
… help us to improve the process of learning biological grammars from protein sequences using Inductive Logic Programming (ILP). Contrary to most traditional ILP learning problems, biological sequences often have a high variation in their length. This variation in length is an important feature …
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Anti-Unification in Constraint Logics: Foundations and Applications to Learnability in First-Order Logic, to Speed-Up Learning, and to Deduction
… several forms of anti-unification in constraint logic, anti-unification relative to background information, are defined, and their semantic and computational properties are studied. It is shown that these forms of anti-unification are applicable to inductive logic programming (inductive learning …
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Fusion: a Visualization Framework for Interactive Ilp Rule Mining With Applications to Bioinformatics
… different types of data. Fusion uses Proteus, an Inductive Logic Programming (ILP) rule finding algorithm to mine relationships in the microarray data. Fusion allows the user to explore the data interactively, choose biases, run the data mining algorithms and visualize the discovered rules. Fusion …
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Envisionment-Based Scheduling Using Time Interval Petri Networks: Representation, Inference, and Learning
… between Petri Nets and Horn clauses by using inductive logic programming methods (ILP) to learn Horn-clauses first and then convert them to TIPNs. The other algorithm employs a general-to-specific search in the space of Petri Net topologies starting with a given initial topology. (5) The …
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Using genetic programming to learn predictive models from spatio-temporal data
… a novel technique called Spatio-Temporal Genetic Programming (STGP). STGP has been compared against the following methods: an Inductive Logic Programming system (Progol), Stochastic Logic Programs, Neural Networks, Bayesian Networks and C4.5, on learning the rules of card games, and predicting a …
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OWL-Miner: Concept Induction in OWL Knowledge Bases
… semantics of OWL is underpinned by Description Logics (DLs), a family of expressive and decidable fragments of first-order logic. Recently, methods of concept induction which are well studied in the field of Inductive Logic Programming have been applied to the related formalism of DLs. These …
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OWL-Miner: Concept Induction in OWL Knowledge Bases
… semantics of OWL is underpinned by Description Logics (DLs), a family of expressive and decidable fragments of first-order logic. Recently, methods of concept induction which are well studied in the field of Inductive Logic Programming have been applied to the related formalism of DLs. These …
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Statistical Relational Learning for Proteomics: Function, Interactions and Evolution
… methods rely on some First- Order Logic as a general, expressive formal language to encode both the data instances and the relations or constraints between them. The latter encode background knowledge on the problem domain, and are use to restrict or bias the model search space …
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A Framework for Combining Logical and Probabilistic Models
… the expressive power of first-order logic with the probabilistic reasoning power of Bayesian networks has attracted the interest of many researchers. We review many techniques for integration of first-order logic and Bayesian networks and propose a new framework that exploits the …