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ALLPAD: approximate learning of logic programs with annotated disjunctions

MACHINE LEARNING 2008年第2-3期70卷 207-223页

作者： Riguzzi, Fabrizio Univ Ferrara Dipartimento Ingn I-44100 Ferrara Italy

logic Programs with Annotated Disjunctions (LPADs) provide a simple and elegant framework for representing probabilistic knowledge in logic programming. In this paper we consider the problem of learning ground LPADs starting from a set of interpretations annotated with their probability. We present the system ALLPAD for solving this problem. ALLPAD modifies the previous system LLPAD in order to tackle real world learning problems more effectively. this is achieved by looking for an approximate solution rather than a perfect one. A number of experiments have been performed on real and artificial data for evaluating ALLPAD, showing the feasibility of the approach.

关键词： inductive logic programming probabilistic logic programming statistical relational learning logic programs with annotated disjunctions

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inductive logic programming for gene regulation prediction

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MACHINE LEARNING 2008年第2-3期70卷 225-240页

作者： Froehler, Sebastian Kramer, Stefan Tech Univ Munich Inst Informat D-85748 Garching Germany

We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In the experiments, the boosted Tilde model is on par with the original model by Middendorf et al. based on alternating decision trees (ADTrees), given the same information. Adding functional categorizations and protein-protein interactions, however, it is possible to improve the performance substantially. We believe that decoding the regulation mechanisms of genes is an exciting new application of learning in logic, requiring data integration from various sources and potentially contributing to a better understanding on a system level.

关键词： inductive logic programming relational learning gene regulation gene expression systems biology

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Compressing probabilistic Prolog programs

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MACHINE LEARNING 2008年第2-3期70卷 151-168页

作者： De Raedt, L. Kersting, K. Kimmig, A. Revoredo, K. Toivonen, H. Katholieke Univ Leuven Dept Computerwetenschappen B-3001 Heverlee Belgium Univ Freiburg Inst Informat D-79100 Freiburg Germany Univ Helsinki Dept Comp Sci FIN-00014 Helsinki Finland

ProbLog is a recently introduced probabilistic extension of Prolog (De Raedt, et al. in Proceedings of the 20th international joint conference on artificial intelligence, pp. 2468-2473, 2007). A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these probabilities are mutually independent. the semantics of ProbLog is then defined by the success probability of a query in a randomly sampled program. this paper introduces the theory compression task for ProbLog, which consists of selecting that subset of clauses of a given ProbLog program that maximizes the likelihood w.r.t. a set of positive and negative examples. Experiments in the context of discovering links in real biological networks demonstrate the practical applicability of the approach.

关键词： probabilistic logic inductive logic programming theory revision compression network mining biological applications statistical relational learning

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inductive logic programming for gene regulation prediction

Inductive logic programming for gene regulation prediction

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16th international conference of inductive logic programming

作者： Froehler, Sebastian Kramer, Stefan Tech Univ Munich Inst Informat D-85748 Garching Germany

关键词： inductive logic programming relational learning gene regulation gene expression systems biology

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Compressing probabilistic Prolog programs

Compressing probabilistic Prolog programs

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16th international conference of inductive logic programming

关键词： probabilistic logic inductive logic programming theory revision compression network mining biological applications statistical relational learning

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Applying inductive logic programming to process mining

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Joint conference of the 17th international conference on inductive logic programming/24th international conference on Machine Learning

作者： Lamma, Evelina Mello, Paola Riguzzi, Fabrizio Storari, Sergio Univ Ferrara ENDIF Via Saragat 1 I-44100 Ferrara Italy Univ Bologna DEIS I-40136 Bologna Italy

ISBN: (纸本)9783540784685

the management of business processes has recently received a lot of attention. One of the most interesting problems is the description of a process model in a language that allows the checking of the compliance of a process execution (or trace) to the model. In this paper we propose a language for the representation of process models that is inspired to the SCIFF language and is an extension of clausal logic. A process model is represented in the language as a set of integrity constraints that allow conjunctive formulas as disjuncts in the head. We present an approach for inducing these models from data: we define a subsumption relation for the integrity constraints, we define a refinement operator and we adapt the algorithm ICL to the problem of learning such formulas. the system has been applied to the problem of inducing the model of a sealed bid auction and of the NetBill protocol. the data used for learning and testing were randomly generated from correct models of the processes.

关键词： process mining learning from interpretations business processes interaction protocols

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Towards an evaluation methodology of diagnostic accuracy for ill-defined domains

Towards an evaluation methodology of diagnostic accuracy for...

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16th international conference on Computers in Education, ICCE 2008

作者： Le, Nguyen-thinh Menzel, Wolfgang Department of Informatics University of Hamburg Germany

ISBN: (纸本)9789868473522

Researchers agree that error diagnosis is one of the most important components of an Intelligent Tutoring System (ITS). Since in ambitious domains a perfect error diagnosis can not be guaranteed, the diagnostic accuracy of an ITS within an ill-defined domain should attract more attention. In this paper we introduce our constraint-based error diagnosis approach for logic programming and demonstrate an evaluation methodology which measures diagnostic accuracy and is comprised of two parts: evaluation of intention analysis and evaluation of diagnostic reliability.

关键词： logic programming

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On the connection between the phase transition of the covering test and the learning success rate in ILP

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MACHINE LEARNING 2008年第2-3期70卷 135-150页

作者： Alphonse, Erick Osmani, Aomar Univ Paris 13 LIPN CNRS UMR 7030 Paris France

It is well-known that heuristic search in ILP is prone to plateau phenomena. An explanation can be given after the work of Giordana and Saitta: the ILP covering test is NP-complete and therefore exhibits a sharp phase transition in its coverage probability. As the heuristic value of a hypothesis depends on the number of covered examples, the regions "yes" and "no" represent plateaus that need to be crossed during search without an informative heuristic value. Several subsequent works have extensively studied this finding by running several learning algorithms on a large set of artificially generated problems and argued that the occurrence of this phase transition dooms every learning algorithm to fail to identify the target concept. We note however that only generate-and-test learning algorithms have been applied and that this conclusion has to be qualified in the case of data-driven learning algorithms. Mostly building on the pioneering work of Winston on near-miss examples, we show that, on the same set of problems, a top-down data-driven strategy can cross any plateau if near-misses are supplied in the training set, whereas they do not change the plateau profile and do not guide a generate-and-test strategy. We conclude that the location of the target concept with respect to the phase transition alone is not a reliable indication of the learning problem difficulty as previously thought.

关键词： inductive logic programming heuristic search plateau phenomena phase transition near-miss examples

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Aspect Mining in Procedural Object-Oriented Code

Aspect Mining in Procedural Object-Oriented Code

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16th IEEE international conference on Program Comprehension

作者： Bhatti, Muhammad Usman Ducasse, Stephane Rashid, Awais Univ Paris 1 Sorbonne CRI Paris France INRIA Lille Nord Europe France Univ Lancaster Dept Comp Lancaster LA1 4YW England

ISBN: (纸本)9780769531762

Although object-oriented programming promotes reusable and well factored entity decomposition, industrial software often shows traces of lack of object-oriented design and procedural thinking. this results in domain entity scattered and tangled code. this is often true in data intensive applications. Aspect mining techniques search for various patterns of scattered and tangled code pertaining to crosscutting concerns. However in the presence of non-abstracted domain logic, the crosscutting concerns identified are inaccurately related to aspects since lack of OO abstraction introduces false positives. this paper identifies the difficulty of identifying crosscutting concerns in systems lacking elementary object-oriented structure. It presents an approach classifying various crosscutting concerns. We report our experience on an industrial software system.

关键词： Object oriented programming

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On the connection between the phase transition of the covering test and the learning success rate in ILP

On the connection between the phase transition of the coveri...

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16th international conference of inductive logic programming

作者： Alphonse, Erick Osmani, Aomar Univ Paris 13 LIPN CNRS UMR 7030 Paris France

关键词： inductive logic programming heuristic search plateau phenomena phase transition near-miss examples

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