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relational instance-based learning with lists and terms

MACHINE learning 2001年第1-2期43卷 53-80页

作者： Horváth, T Wrobel, S Bohnebeck, U Univ Magdeburg Sch Comp Sci IWS D-39106 Magdeburg Germany Univ Bremen Ctr Comp Technol D-28834 Bremen Germany

The similarity measures used in first-order IBL so far have been limited to the function-free case. In this paper we show that a lot of power can be gained by allowing lists and other terms in the input representation and designing similarity measures that work directly on these structures. We present an improved similarity measure for the first-order instance-based learner RIBL that employs the concept of edit distances to efficiently compute distances between lists and terms, discuss its computational and formal properties, and empirically demonstrate its additional power on a problem from the domain of biochemistry. The paper also includes a thorough reconstruction of RIBL'S overall algorithm.

关键词： inductive logic programming relational instance-based learning

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There are plenty of places like home: Using relational representations in hierarchies for distance-based image understanding

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NEUROCOMPUTING 2014年 123卷 75-85页

作者： Antanas, Laura van Otterlo, Martijn Mogrovejo, Jose Oramas Tuytelaars, Tinne De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci B-3001 Heverlee Belgium Radboud Univ Nijmegen NL-6525 ED Nijmegen Netherlands Katholieke Univ Leuven Dept Elect Engn Louvain Belgium

Understanding images in terms of logical and hierarchical structures is crucial for many semantic tasks, including image retrieval, scene understanding and robotic vision. This paper combines robust feature extraction, qualitative spatial relations, relational instance-based learning and compositional hierarchies in one framework. For each layer in the hierarchy, qualitative spatial structures in images are detected, classified and then employed one layer up the hierarchy to obtain higher-level semantic structures. We apply a four-layer hierarchy to street view images and subsequently detect corners, windows, doors, and individual houses. (C) 2013 Elsevier B.V. All rights reserved.

关键词： relational representations relational instance-based learning Hierarchical image understanding

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A prototype-based method for classification with time constraints: a case study on automated planning

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PATTERN ANALYSIS AND APPLICATIONS 2012年第3期15卷 261-277页

作者： Garcia-Duran, Rocio Fernandez, Fernando Borrajo, Daniel Univ Carlos III Madrid Dept Comp Sci Madrid 29011 Spain

The main goal of Nearest Prototype Classification is to reduce storage space and retrieval time of classical instance-based learning (IBL) algorithms. This motivation is higher in relational data since relational distance metrics are much more expensive to compute than classical distances like Euclidean distance. In this paper, we present an algorithm to build relational Nearest Prototype Classifiers (RNPCs). When compared with relational instance-based learning (relational IBL or RIBL) approaches, the algorithm is able to dramatically reduce the number of instances by selecting the most relevant prototypes, maintaining similar accuracy. The number of prototypes is obtained automatically by the algorithm, although it can also be bound by the user. In this work, we also show an application of RNPC for automated planning. Specifically, we describe a modeling task where a relational policy is built following an IBL approach. This approach uses the decisions taken by a planning system as learning examples. We show that when the number of learning examples is reduced with RNPC, the resulting policy is able to scale up better than the original planning system.

关键词： Nearest prototype classification relational learning relational instance-based learning Automated planning

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relational IBL in classical music

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MACHINE learning 2006年第1-3期64卷 5-24页

作者： Tobudic, Asmir Widmer, Gerhard Johannes Kepler Univ Linz Dept Computat Percept A-4040 Linz Austria Austrian Res Inst Artificial Intelligence Vienna Austria

It is well known that many hard tasks considered in machine learning and data mining can be solved in a rather simple and robust way with an instance- and distance-based approach. In this work we present another difficult task: learning, from large numbers of complex performances by concert pianists, to play music expressively. We model the problem as a multi-level decomposition and prediction task. We show that this is a fundamentally relational learning problem and propose a new similarity measure for structured objects, which is built into a relational instance-based learning algorithm named DISTALL. Experiments with data derived from a substantial number of Mozart piano sonata recordings by a skilled concert pianist demonstrate that the approach is viable. We show that the instance-based learner operating on structured, relational data outperforms a propositional k-NN algorithm. In qualitative terms, some of the piano performances produced by DISTALL after learning from the human artist are of substantial musical quality;one even won a prize in an international 'computer music performance' contest. The experiments thus provide evidence of the capabilities of ILP in a highly complex domain such as music.

关键词： relational instance-based learning music

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relational IBL in classical music

Relational IBL in classical music

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13th International Conference on Inductive Logic Programming

作者： Tobudic, Asmir Widmer, Gerhard Johannes Kepler Univ Linz Dept Computat Percept A-4040 Linz Austria Austrian Res Inst Artificial Intelligence Vienna Austria

关键词： relational instance-based learning music

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relational IBL in music with a new structural similarity measure

Relational IBL in music with a new structural similarity mea...

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13th International Conference on Inductive Logic Programming

作者： Tobudic, A Widmer, G Austrian Res Inst Artificial Intelligence Vienna Austria Univ Vienna Dept Med Cybernet & Artificial Intelligence Vienna Austria

ISBN: (纸本)3540201440

It is well known that many hard tasks considered in machine learning and data mining can be solved in an rather simple and robust way with an instance- and distance-based approach. In this paper we present another difficult task: learning, from large numbers of performances by concert pianists, to play music expressively. We model the problem as a multi-level decomposition and prediction task. Motivated by structural characteristics of such a task, we propose a new relational distance measure that is a rather straightforward combination of two existing measures. Empirical evaluation shows that our approach is in general viable and our algorithm, named DISTALL, is indeed able to produce musically interesting results. The experiments also provide evidence of the success of ILP in a complex domain such as music performance: it is shown that our instance-based learner operating on structured, relational data outperforms a propositional k-NN algorithm.

关键词： relational instance-based learning music

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