We describe an algorithm for constructing a set of tree-like conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional level-wise approaches which preserve the monotonicity of frequen...
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We describe an algorithm for constructing a set of tree-like conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional level-wise approaches which preserve the monotonicity of frequency, our block-wise approach preserves monotonicity of feature reducibility and redundancy, which are important in propositionalization employed in the context of classification learning. With pruning based on these properties, our block-wise approach efficiently scales to features including tens of first-order atoms, far beyond the reach of state-of-the art propositionalization or inductive logic programming systems.
Background: Hypotheses are now being automatically produced on an industrial scale by computers in biology, e.g. the annotation of a genome is essentially a large set of hypotheses generated by sequence similarity pro...
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Motivation: Annotated reference corpora play an important role in biomedical information extraction. A semantic annotation of the natural language texts in these reference corpora using formal ontologies is challengin...
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Over the last 25 years there has been considerable body of research into combinations of predicate logic and probability forming what has become known as (perhaps misleadingly) statistical relational artificial intell...
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ISBN:
(纸本)9783642208942;9783642208959
Over the last 25 years there has been considerable body of research into combinations of predicate logic and probability forming what has become known as (perhaps misleadingly) statistical relational artificial intelligence (StaR-AI). I overview the foundations of the area, give some research problems, proposed solutions, outstanding issues, and clear up some misconceptions that have arisen. I discuss representations, semantics, inference and learning, and provide some references to the literature. This is intended to be an overview of foundations, not a survey of research results.
In this paper we look at the discovery of abstract concepts by a robot autonomously exploring its environment and learning the laws of the environment. By abstract concepts we mean concepts that are not explicitly obs...
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ISBN:
(纸本)9783642202810
In this paper we look at the discovery of abstract concepts by a robot autonomously exploring its environment and learning the laws of the environment. By abstract concepts we mean concepts that are not explicitly observable in the measured data, such as the notions of obstacle, stability or a, tool. We consider mechanisms of machine learning that enable the discovery of abstract concepts. Such mechanisms are provided by the logic based approach to machine learning called inductive logic programming (ILP). The feature of predicate invention in ILP is particularly relevant. Examples of actually discovered abstract concepts in experiments are described.
Many computational models of music fail to capture essential aspects of the high-level musical structure and context, and this limits their usefulness, particularly for musically informed users. We describe two recent...
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ISBN:
(纸本)9783642231254
Many computational models of music fail to capture essential aspects of the high-level musical structure and context, and this limits their usefulness, particularly for musically informed users. We describe two recent approaches to modelling musical harmony, using a probabilistic and a logic-based framework respectively, which attempt to reduce the gap between computational models and human understanding of music. The first is a chord transcription system which uses a high-level model of musical context in which chord, key, metrical position, bass note, chroma features and repetition structure are integrated in a Bayesian framework;achieving state-of-the-art performance. The second approach uses inductive logic programming to learn logical descriptions of harmonic sequences which characterise particular styles or genres. Each approach brings us one step closer to modelling music in the way it is conceptualised by musicians.
Despite the potential benefits of asynchronous circuits compared to synchronous circuits, only small advances have been made in the adaptation of asynchronous methodologies by the electronics industry. One of the most...
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ISBN:
(纸本)9780769543833
Despite the potential benefits of asynchronous circuits compared to synchronous circuits, only small advances have been made in the adaptation of asynchronous methodologies by the electronics industry. One of the most important reasons for that, is the lack of asynchronous Electronic Design Automation (EDA) tools and the fact that existing EDA tools are not suitable for asynchronous implementations. Moreover, physical EDA tools, like placement algorithms, involve methodologies which are not applicable to asynchronous circuits, such as static timing analysis (STA) which cannot be performed in a cyclic circuit. In this work, we present CPlace, a constructive placement algorithm which can efficiently handle asynchronous circuits. We use timing separation of events for timing analysis and maintain the quasi-delay insensitive (QDI) properties by bounding the relative delays of wires in isochronic forks. We employ absolute timing constraints for performance and relative timing constraints for QDI which are handled by an ILP formulation. Experimental results show the effectiveness of CPlace in respecting QDI constraints against a synchronous, state-of-the-art industrial placer and a well-known academic placer.
The related theories of Web and Web mining in semantic are concluded and analyzed in this *** description on RDF resource is analyzed on the semantic step,and the clustering method for RDFMS data clustering based on S...
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The related theories of Web and Web mining in semantic are concluded and analyzed in this *** description on RDF resource is analyzed on the semantic step,and the clustering method for RDFMS data clustering based on Semantic distance is proposed,with the detailed description for the algorithm and *** the discussion on the data mining techniques for semantic Web mining,the design on inductive logic programming is proposed for the data mining techniques that are suitable to the semantic ***,how to apply to the description of the algorithm is given through the specific examples to verify the feasibility for the data mining in the semantic Web environment.
This paper explores two different methods of learning dialectal morphology from a small parallel corpus of standard and dialect-form text, given that a computational description of the standard morphology is available...
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ISBN:
(纸本)9781618392473
This paper explores two different methods of learning dialectal morphology from a small parallel corpus of standard and dialect-form text, given that a computational description of the standard morphology is available. The goal is to produce a model that translates individual lexical dialectal items to their standard dialect counterparts in order to facilitate dialectal use of available NLP tools that only assume standard-form input. The results show that a learning method based on inductive logic programming quickly converges to the correct model with respect to many phonological and morphological differences that are regular in nature.
Statistical relational learning (SRL) addresses one of the central open questions of AI: the combination of relational or first-order logic with principled probabilistic and statistical approaches to inference and lea...
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Statistical relational learning (SRL) addresses one of the central open questions of AI: the combination of relational or first-order logic with principled probabilistic and statistical approaches to inference and learning. This thesis approaches SRL from an inductive logic programming (ILP) perspective and starts with developing a general framework for SRL: probabilistic ILP. Based on this foundation, the thesis shows how to incorporate the logical concepts of objects and relations among these objects into Bayesian networks. As time and actions are not just other relations, it afterwards develops approaches to probabilistic ILP over time and for making complex decision in relational domains. Finally, it is shown that SRL approaches naturally yield kernels for structured data. The resulting approaches are illustrated using examples from genetics, bioinformatics, and planning domains.
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