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Sequential inference with reliable observations: Learning to construct force-dynamic models

ARTIFICIAL INTELLIGENCE 2006年第14-15期170卷 1081-1100页

作者： Fern, Alan Givan, Robert Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

We present a trainable sequential-inference technique for processes with large state and observation spaces and relational structure. We apply our technique to the problem of force-dynamic state inference from video, which is a critical component of the LEONARD [J.M. Siskind, Grounding lexical semantics of verbs in visual perception using force dynamics and event logic, Journal of Artificial Intelligence Research 15 (2001) 31-90] visual-event recognition system. LEONARD uses event definitions that are grounded in force-dynamic primitives-making robust and efficient force-dynamic inference critical to good performance. Our sequential-inference method assumes "reliable observations", i.e., that each process state (e.g., force-dynamic state) persists long enough to be reliably inferred from the observations (e.g., video frames) it generates. We introduce the idea of a "state-inference function" (from observation sequences to underlying hidden states) for representing knowledge about a process and develop an efficient sequential-inference algorithm, utilizing this function, that is correct for processes that generate reliable observations consistent with the state-inference function. We describe a representation for state-inference functions in relational domains and give a corresponding supervised learning algorithm. Our experiments in force-dynamic state inference show that our technique provides significantly improved accuracy and speed relative to a variety of recent, hand-coded, non-trainable systems, and a trainable system based on probabilistic modeling. (C) 2006 Published by Elsevier B.V.

关键词： sequence learning relational learning event recognition temporal learning inductive logic programming

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Gene function classification using Bayesian models with hierarchy-based priors

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BMC BIOINFORMATICS 2006年第1期7卷 1-9页

作者： Shahbaba, Babak Neal, Radford M. Univ Toronto Dept Publ Hlth Sci Toronto ON Canada Univ Toronto Dept Stat Toronto ON Canada Univ Toronto Dept Comp Sci Toronto ON Canada

Background: We investigate whether annotation of gene function can be improved using a classification scheme that is aware that functional classes are organized in a hierarchy. The classifiers look at phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and an MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. Results: The results from all three models show substantial improvement over previous methods, which were based on the C5 decision tree algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining the three sources of information in this dataset, our new approach to combining data sources produces a higher accuracy rate than applying our models to each data source alone. Conclusion: Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information.

关键词： Enzyme Commission Multinomial Logit Model inductive logic programming Decision Tree Model Hierarchical Classis

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Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves

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MACHINE LEARNING 2006年第1-3期64卷 231-261页

作者： Goadrich, Mark Oliphant, Louis Shavlik, Jude Univ Wisconsin Dept Biostat & Med Informat Dept Comp Sci Madison WI 53706 USA

Many domains in the field of inductive logic programming (ILP) involve highly unbalanced data. A common way to measure performance in these domains is to use precision and recall instead of simply using accuracy. The goal of our research is to find new approaches within ILP particularly suited for large, highly-skewed domains. We propose Gleaner, a randomized search method that collects good clauses from a broad spectrum of points along the recall dimension in recall-precision curves and employs an "at least L of these K clauses" thresholding method to combine sets of selected clauses. Our research focuses on Multi-Slot Information Extraction (IE), a task that typically involves many more negative examples than positive examples. We formulate this problem into a relational domain, using two large testbeds involving the extraction of important relations from the abstracts of biomedical journal articles. We compare Gleaner to ensembles of standard theories learned by Aleph, finding that Gleaner produces comparable testset results in a fraction of the training time.

关键词： inductive logic programming ensembles recall-precision curves biomedical information extraction

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Markov logic networks

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MACHINE LEARNING 2006年第1-2期62卷 107-136页

作者： Richardson, M Domingos, P Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.

关键词： statistical relational learning Markov networks Markov random fields log-linear models graphical models first-order logic satisfiability inductive logic programming knowledge-based model construction Markov chain Monte Carlo pseudo-likelihood link prediction

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Randomised restarted search in ILP

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MACHINE LEARNING 2006年第1-3期64卷 183-208页

作者： Zelezny, Filip Srinivasan, Ashwin Page, C. David, Jr. Czech Tech Univ CR-16635 Prague Czech Republic IBM India Res Lab New Delhi India Univ Wisconsin Madison WI USA

Recent statistical performance studies of search algorithms in difficult combinatorial problems have demonstrated the benefits of randomising and restarting the search procedure. Specifically, it has been found that if the search cost distribution of the non-restarted randomised search exhibits a slower-than-exponential decay (that is, a "heavy tail"), restarts can reduce the search cost expectation. We report on an empirical study of randomised restarted search in ILP. Our experiments conducted on a high-performance distributed computing platform provide an extensive statistical performance sample of five search algorithms operating on two principally different classes of ILP problems, one represented by an artificially generated graph problem and the other by three traditional classification benchmarks (mutagenicity, carcinogenicity, finite element mesh design). The sample allows us to (1) estimate the conditional expected value of the search cost (measured by the total number of clauses explored) given the minimum clause score required and a "cutoff" value (the number of clauses examined before the search is restarted), (2) estimate the conditional expected clause score given the cutoff value and the invested search cost, and (3) compare the performance of randomised restarted search strategies to a deterministic non-restarted search. Our findings indicate striking similarities across the five search algorithms and the four domains, in terms of the basic trends of both the statistics (1) and (2). Also, we observe that the cutoff value is critical for the performance of the search algorithm, and using its optimal value in a randomised restarted search may decrease the mean search cost (by several orders of magnitude) or increase the mean achieved score significantly with respect to that obtained with a deterministic non-restarted search.

关键词： inductive logic programming randomized search Monte Carlo study

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Complexity parameters for first order classes

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MACHINE LEARNING 2006年第1-3期64卷 121-144页

作者： Arias, Marta Khardon, Roni Columbia Univ Ctr Computat Learning Syst New York NY 10115 USA Tufts Univ Dept Comp Sci Medford MA 02155 USA

We study several complexity parameters for first order formulas and their suitability for first order learning models. We show that the standard notion of size is not captured by sets of parameters that are used in the literature and thus they cannot give a complete characterization in terms of learnability with polynomial resources. We then identify an alternative notion of size and a simple set of parameters that are useful for first order Horn Expressions. These parameters are the number of clauses in the expression, the maximum number of distinct terms in a clause, and the maximum number of literals in a clause. Matching lower bounds derived using the Vapnik Chervonenkis dimension complete the picture showing that these parameters are indeed crucial.

关键词： inductive logic programming learning theory first-order logic VC-dimension query learning

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Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves

Gleaner: Creating ensembles of first-order clauses to improv...

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13th International Conference on inductive logic programming

作者： Goadrich, Mark Oliphant, Louis Shavlik, Jude Univ Wisconsin Dept Biostat & Med Informat Dept Comp Sci Madison WI 53706 USA

关键词： inductive logic programming ensembles recall-precision curves biomedical information extraction

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Guiding the search in the NO region of the phase transition problem with a partial subsumption test

Guiding the search in the NO region of the phase transition ...

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17th European Conference on Machine Learning (ECML 2006)

作者： Wieczorek, Samuel Bisson, Gilles Gordon, Mirta B. CEA DSV DRDC Lab Biol Informat Math F-38054 Grenoble 9 France Imag Lab Grenoble UMR 5522 Lab Leibniz F-38031 Grenoble France

We introduce a test, named pi-subsumption, which computes partial subsumptions between a hypothesis h and an example e, as well as a measure, the subsumption index, which quantifies the covering degree between h and e... 详细信息

ISBN: (纸本)354045375X

关键词： inductive logic programming similarity index theta-subsumption partial subsumption CSP phase transition

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Randomised restarted search in ILP

Randomised restarted search in ILP

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13th International Conference on inductive logic programming

作者： Zelezny, Filip Srinivasan, Ashwin Page, C. David, Jr. Czech Tech Univ CR-16635 Prague Czech Republic IBM India Res Lab New Delhi India Univ Wisconsin Madison WI USA

关键词： inductive logic programming randomized search Monte Carlo study

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Complexity parameters for first order classes

Complexity parameters for first order classes

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13th International Conference on inductive logic programming

作者： Arias, Marta Khardon, Roni Columbia Univ Ctr Computat Learning Syst New York NY 10115 USA Tufts Univ Dept Comp Sci Medford MA 02155 USA

ISBN: (纸本)9783540399179

关键词： inductive logic programming learning theory first-order logic VC-dimension query learning

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