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检索条件"主题词=Probabilistic Graphical Models"

共 398 条记录，以下是211-220 订阅

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Cauchy graphical models 12

Cauchy Graphical Models

引用

12th International Conference on probabilistic graphical models (PGM)

作者： Muvunza, Taurai Li, Yang Kuruoglu, Ercan Engin Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Inst Data & Informat Shenzhen Int Grad Sch Beijing Peoples R China

A common approach to learning Bayesian networks involves specifying an appropriately chosen family of parameterized probability density such as Gaussian. However, the distribution of most real-life data is leptokurtic and may not necessarily be best described by a Gaussian process. In this work we introduce Cauchy graphical models (CGM), a class of multivariate Cauchy densities that can be represented as directed acyclic graphs with arbitrary network topologies, the edges of which encode linear dependencies between random variables. We develop CGLearn, the resultant algorithm for learning the structure and Cauchy parameters based on Minimum Dispersion Criterion (MDC). Experiments using simulated datasets on benchmark network topologies demonstrate the efficacy of our approach when compared to Gaussian graphical models (GGM).

关键词： Cauchy distribution probabilistic graphical models Bayesian networks heavy tails

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A Scientific Inquiry Fusion Theory for High-Level Information Fusion 17

A Scientific Inquiry Fusion Theory for High-Level Informatio...

引用

17th International Conference on Information Fusion (FUSION)

作者： Ao, Zhuoyun Scholz, Jason Oxenham, Martin Joint and Operations Analysis Division Defence Science Technology Organisation South Australia Australia

ISBN: (纸本)9781479916344

The Joint Directors of Laboratories fusion model is adequate as a functional description, but falls short as a formal design guide for the application of logical inference under uncertainty for high-level information fusion. We propose a formal construct called the Scientific Inquiry Fusion Theory, with three stages of explanation, prediction and generalisation aligned with the corresponding inferences of abduction, deduction and induction. We first define fusion as formal models without uncertainty, in which the corresponding logical inference patterns can be used for solving fusion problems. Then, we extend these formal models with uncertainty through probabilistic graphical models, where fusion processes are realised by statistical queries and learning algorithms based on the sound unification of Probability Theory, Mathematical Logic and Machine Learning. Finally, we demonstrate the application of this formal high-level information fusion framework with an example of automated maritime security situation and threat assessment.

关键词： statistical relational learning frameworks for multi-level fusion processes situation and intent assessment behaviour classification probabilistic graphical models logic-based fusion

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The PC-Algorithm of the Algebraic Bayesian Network Secondary Structure Training 1

引用

19th Russian Conference on Artificial Intelligence (RCAI)

作者： Kharitonov, Nikita Abramov, Maxim Tulupyev, Alexander St Petersburg State Univ St Petersburg Russia Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg Russia

ISBN: (数字)9783030868550

ISBN: (纸本)9783030868550;9783030868543

Algebraic Bayesian networks and Bayesian belief networks are one of the probabilistic graphical models. One of the main tasks which need to be solved during the networks' handling is the model structure training. This paper is dedicated to the automation of this process for algebraic Bayesian networks. This work relates to the PC-algorithm for algebraic Bayesian network secondary structure training. The algorithm is based on the PC-algorithm for Belief Bayesian networks training. The algorithm pseudocode and usage example are described. The provided algorithm helps investigate the full-automated machine learning of algebraic Bayesian networks. Earlier, the structure was provided by experts.

关键词： Algebraic bayesian networks Bayesian belief networks probabilistic graphical models Structure training PC-algorithm

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Protein Secondary Structure Prediction using Large Margin Methods

Protein Secondary Structure Prediction using Large Margin Me...

引用

8th IEEE/ACIS International Conference on Computer and Information Science

作者： Tang, Buzhou Wang, Xuan Wang, Xiaolong Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China

ISBN: (纸本)9780769536415

Protein secondary structure prediction is an important step to understanding protein tertiary structure. Recent studies indicate that the correlation between neighboring secondary structures are beneficial to improve prediction performance. In this paper, we propose a new large margin approach for protein secondary structure prediction, which consider the problem as a sequence labeling problem like probabilistic graphical models. It doesn't only make full use of the correlation between neighboring secondary structures like graphical chain models, but also shares the key advantages of other SVM-based methods, i.e. learming non-linear discriminant via kernel functions. The experimental results on datasets: CB513 and RS126 show that our algorithm outperforms other state-of-the-art methods.

关键词： Protein secondary structure prediction large margin approach sequence labeling problem probabilistic graphical models

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WHAT COULD BE ACHIEVED WITH A MILLION QUBITS QUANTUM ANNEALER IN REMOTE SENSING?

WHAT COULD BE ACHIEVED WITH A MILLION QUBITS QUANTUM ANNEALE...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Gawron, Piotr Sadowski, Przemyslaw Glomb, Przemyslaw Gardas, Bartlomiej van Waveren, Matthijs Forray, Clement Pasero, Guillaume Savinaud, Mickael Brunet, Pierre-Marie Faucoz, Orphee Puchala, Zbigniew Pawela, Lukasz PAS Nicolaus Copernicus Astron Ctr AstroCeNT Rektorska 4 PL-00614 Warsaw Poland PAS Inst Theoret & Appl Informat Baltycka 5 PL-44100 Gliwice Poland CS GRP 6 Rue Brindejonc Moulinais F-31506 Toulouse France CNES 10 Ave Edouard Belin F-31401 Toulouse France

ISBN: (纸本)9798350360332;9798350360325

We discuss the applicability of large quantum annealers for the purpose of processing Remote Sensing images. We show an application of currently existing quantum annealers for the purpose of post-processing segmentation of a hyperspectral image. We show that in principle there might exist useful applications of large scale quantum annealers for the purpose of Remote Sensing data processing.

关键词： image segmentation probabilistic graphical models energy-based models quantum annealing

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A Countable State PGM for Tracking Entity Movement 15

A Countable State PGM for Tracking Entity Movement

引用

15th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Zajic, Tim Raytheon Integrated Def Syst 235 Presidential Way Woburn MA 01801 USA

ISBN: (纸本)9781509061679

We consider a probabilistic graphical model for the problem of tracking entities moving among a finite set of sites. The observations consist of counts of the number of entities at sites and during movement between sites. A Bayesian approach is adopted and an importance sampling approach taken to obtaining samples from the model. A backtrack-free proposal distribution is considered and an oracle is obtained through the construction of appropriate network flow problems.

关键词： Tracking probabilistic graphical models Importance Sampling Backtrack-Free Proposal Distribution

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SPATIAL-TEMPORAL CONDITIONAL RANDOM FIELD BASED MODEL FOR CROP RECOGNITION IN TROPICAL REGIONS 37

SPATIAL-TEMPORAL CONDITIONAL RANDOM FIELD BASED MODEL FOR CR...

引用

IEEE International Geoscience & Remote Sensing Symposium

作者： Achanccaray, P. Feitosa, R. Q. Rottensteiner, F. Sanches, I. D. Heipke, C. Pontifical Catholic Univ Rio de Janeiro Dept Elect Engn Rio De Janeiro Brazil Univ Estado Rio De Janeiro Dept Comp Engn Rio De Janeiro Brazil Leibniz Univ Hannover Inst Photogrammetry & GeoInformat Hannover Germany Natl Inst Space Res Remote Sensing Div Sao Jose Dos Campos Brazil

ISBN: (纸本)9781509049516

This work presents a spatio-temporal Conditional Random Field (CRF) based model for crop recognition from multi-temporal remote sensing image sequences. The association potential at each image site is based on the class posterior probabilities computed by a Random Forest (RF) classifier given the features at the corresponding site. A contrast-sensitive Potts model is used as a label smoothing method in the spatial domain, whereas the interactions in the temporal domain are modeled based on expert knowledge about the possible transitions between adjacent epochs. The CRF based model was tested for crop mapping in two subtropical areas based on a sequences of 9 Landsat and 14 Sentinel-1 images from Ipua, Sao Paulo and Campo Verde, Mato Grosso, respectively, two municipalities in Brazil. The experiments showed significant improvements of the accumulated F1 score per class against a mono-temporal CRF approach of up to 50% and 75% for a total of 8 and 11 classes using Optical and SAR images respectively.

关键词： remote sensing probabilistic graphical models crop recognition Landsat images Sentinel-1

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Multi-Label Inference for Crowdsourcing 18

Multi-Label Inference for Crowdsourcing

引用

24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Zhang, Jing Wu, Xindong Nanjing Univ Sci & Technol Sch Comp Sci & Engn 200 Xiaolingwei St Nanjing 210094 Jiangsu Peoples R China Univ Louisiana Lafayette Sch Comp & Informat 222 James R Oliver Hall Lafayette LA 70504 USA

ISBN: (纸本)9781450355520

When acquiring labels from crowdsourcing platforms, a task may be designed to include multiple labels and the values of each label may belong to a set of various distinct options, which is the so-called multi-class multi-label annotation. To improve the quality of labels, one task is independently completed by a group of heterogeneous crowdsourced workers. Then, the true values of the multiple labels of each task are inferred from these repeated noisy labels. In this paper, we propose a novel probabilistic method, which includes a multi-class multi-label dependency (MCMLD) model, to address this problem. The proposed method assumes that the label-correlation exists in both unknown true labels and noisy crowdsourced labels. Thus, it introduces a mixture of multiple independently multinoulli distributions to capture the correlation among the labels. Finally, the unknown true values of the multiple labels of each task, together with a set of confusion matrices modeling the reliability of the workers, can be jointly inferred through an EM algorithm. Experiments with three simulated typical crowdsourcing scenarios and a real-world dataset consistently show that our proposed MCMLD method significantly outperforms several competitive alternatives. Furthermore, if the labels are strongly correlated, the advantage of MCMLD will be more remarkable.

关键词： Crowdsourcing label aggregation maximum likelihood estimation mixture models probabilistic graphical models

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Locally averaged Bayesian Dirichlet metrics for learning the structure and the parameters of Bayesian networks

Locally averaged Bayesian Dirichlet metrics for learning the...

引用

11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSARU)

作者： Cano, Andres Gomez-Olmedo, Manuel Masegosa, Andres R. Moral, Serafin Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

The marginal likelihood of the data computed using Bayesian score metrics is at the core of score+search methods when learning Bayesian networks from data. However, common formulations of those Bayesian score metrics rely on free parameters which are hard to assess. Recent theoretical and experimental works have also shown that the commonly employed BDe score metric is strongly biased by the particular assignments of its free parameter known as the equivalent sample size. This sensitivity means that poor choices of this parameter lead to inferred BN models whose structure and parameters do not properly represent the distribution generating the data even for large sample sizes. In this paper we argue that the problem is that the BDe metric is based on assumptions about the BN model parameters distribution assumed to generate the data which are too strict and do not hold in real settings. To overcome this issue we introduce here an approach that tries to marginalize the meta-parameter locally, aiming to embrace a wider set of assumptions about these parameters. It is shown experimentally that this approach offers a robust performance, as good as that of the standard BDe metric with an optimum selection of its free parameter and, in consequence, this method prevents the choice of wrong settings for this widely applied Bayesian score metric. (C) 2012 Elsevier Inc. All rights reserved.

关键词： probabilistic graphical models Bayesian networks Structure learning Parameter estimation Bayesian metrics

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Unifying Logical and Statistical AI 16

Unifying Logical and Statistical AI

引用

31st Annual ACM-IEEE Symposium on Logic in Computer Science (LICS)

作者： Domingos, Pedro Lowd, Daniel Kok, Stanley Nath, Aniruddh Poon, Hoifung Richardson, Matthew Singla, Parag Univ Washington Seattle WA 98195 USA Univ Oregon Eugene OR 97403 USA Singapore Univ Technol & Design Singapore Singapore Google Mountain View CA USA Microsoft Res Mountain View CA USA Indian Inst Technol Delhi India

ISBN: (纸本)9781450343916

Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledge-based model construction. Learning algorithms are based on the voted perceptron, pseudo-likelihood and inductive logic programming. Markov logic has been successfully applied to a wide variety of problems in natural language understanding, vision, computational biology, social networks and others, and is the basis of the open-source Alchemy system.

关键词： Markov networks first-order logic probabilistic graphical models probabilistic inference Markov chain Monte Carlo machine learning

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