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International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE)

作者： Patel, Dhruv B. Goswami, Mukesh M. Dharmsinh Desai Univ Dept Informat Technol Nadiad India

ISBN: (纸本)9781479949816

Post processing is an important part of any document processing system. There are two ways of post processing. First word level correction and second sentence level correction in document. The word level is performed in two ways first, finding error and finding dictionary by most similar word. That is called dictionary based approach. Another method to find most probable word is known as probabilistic approach. In order to generate the probabilistic model which includes unigram, bigram, trigram, online resources from various Gujarati newspaper websites are used. The proposed system will use models like Naive Bayes and Hidden Markov model to correct word level error. The system will be tested on synthetic dataset which is generated by adding random word level error in the actual document.

关键词： Naive Bayes Hidden Markov model probabilistic graphical model

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Inferring Correspondences from Multiple Sources for Microblog User Tags 3

Inferring Correspondences from Multiple Sources for Microblo...

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3rd National Conference on Social Media Processing (SMP)

作者： Tu, Cunchao Liu, Zhiyuan Sun, Maosong Tsinghua Univ Natl Lab Informat Sci & Technol State Key Lab Intelligent Technol & Syst Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9783662455579

Some microblog services encourage users to annotate themselves with multiple tags, indicating their attributes and interests. User tags play an important role for personalized recommendation and information retrieval. In order to better understand the semantics of user tags, we propose Tag Correspondence model (TCM) to identify complex correspondences of tags from the rich context of microblog users. In TCM, we divide the context of a microblog user into various sources (such as short messages, user profile, and neighbors). With a collection of users with annotated tags, TCM can automatically learn the correspondences of user tags from the multiple sources. With the learned correspondences, we are able to interpret implicit semantics of tags. Moreover, for the users who have not annotated any tags, TCM can suggest tags according to users' context information. Extensive experiments on a real-world dataset demonstrate that our method can efficiently identify correspondences of tags, which may eventually represent semantic meanings of tags.

关键词： User Tag Suggestion Tag Correspondence model probabilistic graphical model

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Speeding Up Gibbs Sampling in probabilistic Optical Flow

Speeding Up Gibbs Sampling in Probabilistic Optical Flow

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作者： Dongzhen Piao Carnegie Mellon University

学位级别：博士

In today's machine learning research, probabilistic graphical models are used ex- tensively to model complicated systems with uncertainty, to help understanding of the problems, and to help inference and predict unknown events. For inference tasks, exact inference methods such as junction tree algorithms exist, but they su? er from expo- nential growth of cluster size and thus is not able to handle large and highly connected graphs. Approximate inference methods do not try to find exact probabilities, but rather give results that improve as algorithm runs. Gibbs sampling, as one of the approximate inference methods, has gained lots of traction and is used extensively in inference tasks, due to its ease of understanding and implementation. However, as problem size grows, even the faster algorithm needs a speed boost to meet application requirement. The number of variables in an application graphical model can range from tens of thousands to billions, depending on problem domain. The original sequential Gibbs sampling may not return satisfactory result in limited time. Thus, in this thesis, we investigate in ways to speed up Gibbs sampling. We will study ways to do better initialization, blocking variables to be sampled together, as well as using simulated annealing. These are the methods that modifies the algorithm itself. We will also investigate in ways to parallelize the algorithm. An algorithm is paral- lelizable if some steps do not depend on other steps, and we will find out such dependency in Gibbs sampling. We will discuss how the choice of di? erent hardware and software architecture will a? ect the parallelization result. We will use optical flow problem as an example to demonstrate the various speed up methods we investigated. An optical flow method tries to find out the movements of small image patches between two images in a temporal sequence. We demonstrate how we can model it using probabilistic graphical model, and solve it using Gibbs sampling. The r

关键词： Gibbs Sampling Optical Flow Parallelization Speed Up probabilistic graphical model Markov Random Field Simulated Annealing

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A Hardware Generator for Factor Graph Applications

A Hardware Generator for Factor Graph Applications

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作者： Demma, James Daniel Virginia Tech | University

A Factor Graph (FG -- http://***/wiki/Factor_graph) is a structure used to find solutions to problems that can be represented as a probabilistic graphical model (PGM). They consist of interconnected variable nodes and factor nodes, which iteratively compute and pass messages to each other. FGs can be applied to solve decoding of forward error correcting codes, Markov chains and Markov Random Fields, Kalman Filtering, Fourier Transforms, and even some games such as Sudoku. In this paper, a framework is presented for rapid prototyping of hardware implementations of FG-based applications. The FG developer specifies aspects of the application, such as graphical structure, factor computation, and message passing algorithm, and the framework returns a design. A system of Python scripts and Verilog Hardware Description Language templates together are used to generate the HDL source code for the application. The generated designs are vendor/platform agnostic, but currently target the Xilinx Virtex-6-based ML605. The framework has so far been primarily applied to construct Low Density Parity Check (LDPC) decoders. The characteristics of a large basket of generated LDPC decoders, including contemporary 802.11n decoders, have been examined as a verification of the system and as a demonstration of its capabilities. As a further demonstration, the framework has been applied to construct a Sudoku solver.

关键词： Factor Graph probabilistic graphical model Digital Design Sum-Product Min-Sum Belief Propagation Hardware Generator Thesis

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probabilistic graphical models for Flood State Detection of Roads Combining Imagery and DEM

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2012年第6期9卷 1051-1055页

作者： Frey, Daniel Butenuth, Matthias Straub, Daniel Tech Univ Munich Chair Remote Sensing Technol D-80333 Munich Germany Tech Univ Munich Engn Risk Anal Grp D-80333 Munich Germany

A new system for estimating the state of roads during flooding based on probabilistic graphical models is presented. The location of the roads is given by a geographic information system, whereas the up-to-date information for the assessment of flood state is delivered by remote sensing data. Furthermore, the height information from a digital elevation model (DEM) is combined with image data to improve the accuracy of the results. The presented system is based on factor graphs, which are used to model the statistical dependence between random variables. Three different models are presented: a 1-D pixel-based model, a 2-D topology-based model considering the dependences of neighboring pixels, and a 3-D multitemporal-based model, which can deal with sequential remote sensing imagery at several points in time. The proposed models are compared to a flood simulation based only on the DEM and a maximum likelihood classification based only on the image data. A numerical evaluation demonstrates the improved performance of the three proposed models.

关键词： Bayesian network (BN) detection factor graph flooding probabilistic graphical model

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Multi-Matrices Factorization with Application to Missing Sensor Data Imputation

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SENSORS 2013年第11期13卷 15172-15186页

作者： Huang, Xiao-Yu Li, Wubin Chen, Kang Xiang, Xian-Hong Pan, Rong Li, Lei Cai, Wen-Xue Sun Yat Sen Univ Software Inst Guangzhou 510275 Guangdong Peoples R China S China Univ Technol Sch Econ & Commerce Guangzhou 510006 Guangdong Peoples R China Umea Univ Dept Comp Sci SE-90187 Umea Sweden Acad Guangdong Telecom Co Ltd Guangzhou 510630 Guangdong Peoples R China Sun Yat Sen Univ Affiliated Hosp 1 Dept Intervent Radiol Guangzhou 510080 Guangdong Peoples R China

We formulate a multi-matrices factorization model (MMF) for the missing sensor data estimation problem. The estimation problem is adequately transformed into a matrix completion one. With MMF, an n-by-t real matrix, R, is adopted to represent the data collected by mobile sensors from n areas at the time, T-1, T-2, . . . , T-t, where the entry, R-i,R-j, is the aggregate value of the data collected in the ith area at T-j. We propose to approximate R by seeking a family of d-by-n probabilistic spatial feature matrices, U-(1), U-(2), . . . , U-(t), and a probabilistic temporal feature matrix, V epsilon R-dxt, where R-j approximate to U-(j)(T) T-j. We also present a solution algorithm to the proposed model. We evaluate MMF with synthetic data and a real-world sensor dataset extensively. Experimental results demonstrate that our approach outperforms the state-of-the-art comparison algorithms.

关键词： matrix factorization sensor data probabilistic graphical model missing estimation

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AN ADAPTIVE DECISION METHOD USING STRUCTURE FEATURE ANALYSIS ON DYNAMIC FAULT PROPAGATION model 12

AN ADAPTIVE DECISION METHOD USING STRUCTURE FEATURE ANALYSIS...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Dong, Chun-Ling Zhang, Qin Zhao, Yue Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Tsinghua Univ Inst Nucl & New Energy Technol Beijing 100083 Peoples R China Shandong Normal Univ Dept GIS Jinan 250014 Peoples R China

ISBN: (纸本)9781479902606

Effective online maintenance decisions for the troubleshooting of complex systems can avoid fault progressions and reduce potential losses. Inspired by the inhomogeneous topological nature of complex networks, in this study we intend to explore the pivotal node with high failure pervasion ability, so as to formulate an adaptive decision-making method. Dynamic Uncertain Causality Graph is introduced as the fault propagation model for evolving causalities. In order to evaluate the inherent topological structure feature of failure mode, the between centrality and non-symmetrical entropy are incorporated in the fault spreading risk measurement of nodes. Benefiting from solutions of time-varying structure decomposition and causality reduction on fault propagation model, the decision-making algorithm based on local causality structure achieves globally high efficiency and scalability. Verification experiments using generator faults of a nuclear power plant indicate the feasibility of this method in large-scale industrial applications.

关键词： probabilistic graphical model adaptive decision-making causality representation uncertain inference structure feature analysis causal influence assessment

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DATA FUSION FOR BUILDING RECONSTRUCTION FROM MULTI-ASPECT INSAR DATA

DATA FUSION FOR BUILDING RECONSTRUCTION FROM MULTI-ASPECT IN...

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International-Society-for-Photogrammetry-and-Remote-Sensing Hannover Workshop

作者： Maksymiuk, O. Stilla, U. Tech Univ Munich Photogrammetry & Remote Sensing D-80333 Munich Germany

ISBN: (纸本)9781629935201

In this paper we present a novel approach for 3D reconstruction of point clouds based on single baseline Multi-Aspect InSAR (MASAR) data. The point clouds represent an intermediate result to achieve a comprehensive building reconstruction framework. The exact determination of scatterers based on SAR data is a non-trivial task since the optimal solution requires the knowledge of the number of scatterers within one range cell. In recent years many methods were proposed addressing this problem but most of them require multiple observations making them inapplicable to our task. We use a probabilistic graphical model (PGM) to combine all aspects into a common framework exploiting all contradictions and redundancy in the data. The model is used iteratively together with local optimizations adjusting the hypothesis of the scatterer within one range cell to the corresponding observation. This makes it possible to find a global solution.

关键词： InSAR Multi-Aspect MASAR 3D Reconstruction Building Reconstruction Urban Areas probabilistic graphical model Data Fusion Radargrammetry Compressive Sensing Sparsity

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Ontology Learning from Incomplete Semantic Web Data by BelNet

Ontology Learning from Incomplete Semantic Web Data by BelNe...

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25th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Zhu, Man Gao, Zhiqiang Pan, Jeff Z. Zhao, Yuting Xu, Ying Quan, Zhibin Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Univ Aberdeen Dept Comp Sci Aberdeen Scotland

ISBN: (纸本)9781479929719

Recent years have seen a dramatic growth of semantic web on the data level, but unfortunately not on the schema level, which contains mostly concept hierarchies. The shortage of schemas makes the semantic web data difficult to be used in many semantic web applications, so schemas learning from semantic web data becomes an increasingly pressing issue. In this paper we propose a novel schemas learning approach - BelNet, which combines description logics (DLs) with Bayesian networks. In this way BelNet is capable to understand and capture the semantics of the data on the one hand, and to handle incompleteness during the learning procedure on the other hand. The main contributions of this work are: (i) we introduce the architecture of BelNet, and correspondingly propose the ontology learning techniques in it;(ii) we compare the experimental results of our approach with the state-of-the-art ontology learning approaches, and provide discussions from different aspects.

关键词： Ontology learning probabilistic graphical model Semantic web

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A review on probabilistic graphical models in evolutionary computation

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JOURNAL OF HEURISTICS 2012年第5期18卷 795-819页

作者： Larranaga, Pedro Karshenas, Hossein Bielza, Concha Santana, Roberto Univ Politecn Madrid Fac Informat Computat Intelligence Grp E-28660 Madrid Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp San Sebastin 20080 Donostia Spain

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.

关键词： probabilistic graphical model Bayesian network Evolutionary computation Estimation of distribution algorithm

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