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检索条件"任意字段=International Conference on Probabilistic Graphical Models"

共 872 条记录，以下是91-100 订阅

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A Framework Based on graphical models with Logic for Chinese Named Entity Recognition 3

A Framework Based on Graphical Models with Logic for Chinese...

引用

3rd international Joint conference on Natural Language Processing, IJCNLP 2008

作者： Yu, Xiaofeng Lam, Wai Chan, Shing-Kit Information Systems Laboratory Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong

Chinese named entity recognition (NER) has recently been viewed as a classification or sequence labeling problem, and many approaches have been proposed. However, they tend to address this problem without considering linguistic information in Chinese NEs. We propose a new framework based on probabilistic graphical models with firstorder logic for Chinese NER. First, we use Conditional Random Fields (CRFs), a standard and theoretically well-founded machine learning method based on undirected graphical models as a base system. Second, we introduce various types of domain knowledge into Markov Logic Networks (MLNs), an effective combination of first-order logic and probabilistic graphical models for validation and error correction of entities. Experimental results show that our framework of probabilistic graphical models with first-order logic significantly outperforms the state-of-the-art models for solving this task. © 2008 IJCNLP 2008 - 3rd international Joint conference on Natural Language Processing, Proceedings of the conference. All rights reserved.

关键词： Error correction

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Serving MPE Queries on Tensor Networks by Computing Derivatives 12

Serving MPE Queries on Tensor Networks by Computing Derivati...

引用

12th international conference on probabilistic graphical models (PGM)

作者： Wenig, Maurice Barschel, Hanno Giesen, Joachim Goral, Andreas Blacher, Mark Friedrich Schiller Univ Jena Jena Germany

Recently, tensor networks have been proposed as a data structure for weighted model counting. Computing a weighted model count is thus reduced to contracting a factorized tensor expression. Inference queries on graphical models, especially PoE (probability of evidence) queries, can be expressed directly as weighted model counting problems. Maximization problems can also be addressed on the same data structure, only the standard sum-product semiring has to be replaced by either the tropical (max-sum) or the Viterbi (max-product) semiring in the computations, that is, the tensor contractions. However, tensor contractions only provide maximal values, but MPE (most probable explanation) queries on graphical models do not ask for the maximal value, but for a state, or even the states, at which the maximal value is attained. In the special case of tropical tensor networks for ground states of spin glasses, it has been observed that the ground state can be obtained by computing a derivative of the tensor network over the tropical semiring. Here, we generalize this observation, provide a generic algorithm for computing the derivatives, and prove its correctness.

关键词： graphical models Tensor Networks Most Probable Explanation (MPE) Maximum Probability States Tensor Derivatives

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graphical probabilistic modeling and applications in multimedia content analysis 11

Graphical probabilistic modeling and applications in multime...

引用

19th ACM international conference on Multimedia ACM Multimedia 2011, MM'11

作者： Zhang, Xiao-Ping Liu, Zhu Ryerson University Toronto Canada ATandT Labs - Research Middletown NJ United States

ISBN: (纸本)9781450306164

graphical probabilistic models play an important role in modern machine learning and pattern recognition. In this half-day tutorial, we introduce the fundamentals of graphical probabilistic modeling, including Bayesian networks, Markov random fields, hidden conditional Random field, etc. We also discuss some applications of graphical probabilistic modeling in multimedia content analysis, for example, video segmentation, video event detection, video sequence matching, and image labeling. The audience will learn the basic of graphical models, and get familiar with the state of the art in multimedia content analysis systems. © 2011 ACM.

关键词： Bayesian networks

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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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Mining relationship between video concepts using probabilistic graphical models

Mining relationship between video concepts using probabilist...

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IEEE international conference on Multimedia and Expo (ICME 2006)

作者： Yan, Rong Chen, Ming-yu Hauptmann, Alexander Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA

ISBN: (纸本)9781424403660

For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. These semantic concepts do not exist in isolation to each other and exploiting this relationship between multiple video concepts could be a useful source to improve the concept detection accuracy. In this paper, we describe various multi-concept relational learning approaches via a unified probabilistic graphical model representation and propose using numerous graphical models to mine the relationship between video concepts that have not been applied before. Their performances in video semantic concept detection are evaluated and compared on two TRECVID'05 video collections.

关键词： Data mining

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Automated probabilistic Modeling for Relational Data 13

Automated Probabilistic Modeling for Relational Data

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22nd ACM international conference on Information and Knowledge Management (CIKM)

作者： Singh, Sameer Graepel, Thore Univ Massachusetts Sch Comp Sci Amherst MA 01003 USA Microsoft Res Cambridge England

ISBN: (纸本)9781450322638

probabilistic graphical model representations of relational data provide a number of desired features, such as inference of missing values, detection of errors, visualization of data, and probabilistic answers to relational queries. However, adoption has been slow due to the high level of expertise expected both in probability and in the domain from the user. Instead of requiring a domain expert to specify the probabilistic dependencies of the data, we present an approach that uses the relational DB schema to automatically construct a Bayesian graphical model for a database. This resulting model contains customized distributions for the attributes, latent variables that cluster the records, and factors that reflect and represent the foreign key links, whilst allowing efficient inference. Experiments demonstrate the accuracy of the model and scalability of inference on synthetic and real-world data.

关键词： Bayesian models probabilistic Modeling Databases

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Efficient Heuristic Search for M-Modes Inference 10

Efficient Heuristic Search for M-Modes Inference

引用

10th international conference on probabilistic graphical models (PGM)

作者： Chen, Cong Yuan, Changhe Chen, Chao CUNY New York NY 10021 USA SUNY Stony Brook Stony Brook NY 11794 USA

M-Modes is the problem of finding the top M locally optimal solutions of a graphical model, called modes. These modes provide geometric characterization of the energy landscape of a graphical model and lead to high quality solutions in structured prediction. It has been shown that any mode must be a local MAP within every subgraph of certain size. The state-of-the-art method is a search algorithm that explores subgraphs in a fixed ordering, uses each subgraph as a layer and searches for a consistent concatenation of local MAPs. We observe that for the M-Modes problem, different search orderings can lead to search spaces with dramatically different sizes, resulting in huge differences in performance. We formalize a metric measuring the quality of different orderings. We then formulate finding an optimized ordering as a shortest path problem, and introduce pruning criteria to speed up the search. Our empirical results show that using optimized orderings improves the efficiency of M-Modes search by up to orders of magnitude.

关键词： graphical Model Exact Inference Heuristic Search M-Modes

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A Unified probabilistic graphical Model based Approach for the Robust Decoding of Color Structured Light Pattern 4

A Unified Probabilistic Graphical Model based Approach for t...

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4th IEEE international conference on Information Science and Technology (ICIST)

作者： Yang, Chao Liu, Fang Song, Zhan Chinese Univ Hong Kong Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Guangdong Peoples R China China Air Force Logist Coll Dept Aviat Ammunit Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781479948086

Color coding is an important research topic in spatial encoded structured light sensing (SLS). In this study, we propose a novel graphical model based approach for the color pattern decoding task. For efficient color labeling, the color pattern is firstly decomposed into separate binary pattern images. With the labeled pattern elements, a unified probabilistic graphical framework is constructed to represent the pseudorandom pattern as a clique tree structure. The model contains two parts: the Conditional Random Field (CRF) is used to represent the dependences between these local decisions, and the Bayesian network (BN) is applied for the representation of background colors effect. A colorful target is experimented to demonstrate its feasibility. And the 3D reconstructed models based on the decoding results are also provided to show its robustness.

关键词： probabilistic graphical model structured light sensing pseudorandom array pattern decoding

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Encoding probabilistic graphical models into Stochastic Boolean Satisfiability 31

Encoding Probabilistic Graphical Models into Stochastic Bool...

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31st international Joint conference on Artificial Intelligence (IJCAI)

作者： Hsieh, Cheng-Han Jiang, Jie-Hong R. Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9781956792003

Statistical inference is a powerful technique in various applications. Although many statistical inference tools are available, answering inference queries involving complex quantification structures remains challenging. Recently, solvers for Stochastic Boolean Satisfiability (SSAT), a powerful formalism allowing concise encodings of PSPACE decision problems under uncertainty, are under active development and applied in more and more applications. In this work, we exploit SSAT solvers for the inference of probabilistic graphical models (PGMs), an essential representation for probabilistic reasoning. Specifically, we develop encoding methods to systematically convert PGM inference problems into SSAT formulas for effective solving. Experimental results demonstrate that, by using our encoding, SSAT-based solving can complement existing PGM tools, especially in answering complex queries.

关键词： Graphic methods

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probabilistic graphical models and Deep Belief Networks for Prognosis of Breast Cancer 14

Probabilistic Graphical Models and Deep Belief Networks for ...

引用

IEEE 14th international conference on Machine Learning and Applications ICMLA

作者： Khademi, Mahmoud Nedialkov, Nedialko S. McMaster Univ Dept Comp & Software Hamilton ON Canada

ISBN: (纸本)9781509002870

We propose a probabilistic graphical model (PGM) for prognosis and diagnosis of breast cancer. PGMs are suitable for building predictive models in medical applications, as they are powerful tools for making decisions under uncertainty from big data with missing attributes and noisy evidence. Previous work relied mostly on clinical data to create a predictive model. Moreover, practical knowledge of an expert was needed to build the structure of a model, which may not be accurate. In our opinion, since cancer is basically a genetic disease, the integration of microarray and clinical data can improve the accuracy of a predictive model. However, since microarray data is high-dimensional, including genomic variables may lead to poor results for structure and parameter learning due to the curse of dimensionality and small sample size problems. We address these problems by applying manifold learning and a deep belief network (DBN) to microarray data. First, we construct a PGM and a DBN using clinical and microarray data, and extract the structure of the clinical model automatically by applying a structure learning algorithm to the clinical data. Then, we integrate these two models using softmax nodes. Extensive experiments using real-world databases, such as METABRIC and NKI, show promising results in comparison to Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN) classifiers, for classifying tumors and predicting events like recurrence and metastasis.

关键词： probabilistic graphical models deep belief networks breast cancer microarray data

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