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Approximate credal network updating by linear programming with applications to decision making

international JOURNAL OF APPROXIMATE REASONING 2015年 58卷 25-38页

作者： Antonucci, Alessandro de Campos, Cassio P. Huber, David Zaffalon, Marco Ist Dalle Molle Studi Intelligenza Artificiale ID CH-6928 Lugano Switzerland Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Comp Sci Elmwood Belfast BT9 6AZ Antrim North Ireland

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. An algorithm for approximate credal network updating is presented. the problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. this simple idea can be iterated and quickly leads to accurate inferences. A transformation is also derived to reduce decision making in credal networks based on the maximality criterion to updating. the decision task is proved to have the same complexity of standard inference, being NPPP-complete for general credal nets and NP-complete for polytrees. Similar results are derived for the E-admissibility criterion. Numerical experiments confirm a good performance of the method. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Credal networks Bayesian networks Linear programming Decision making Maximality E-admissibility

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Data graph formulation as the minimum-weight maximum-entropy problem 10

Data graph formulation as the minimum-weight maximum-entropy...

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10th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2015

作者： De Sousa, Samuel Kropatsch, Walter G. Pattern Recognition and Image Processing Group Vienna University of Technology Vienna Austria

ISBN: (纸本)9783319182230

Consider a point-set coming from an object which was sampled using a digital sensor (depth range, camera, etc). We are interested in finding a graph that would represent that point-set according to some properties. Such a representation would allow us to match two objects (graphs) by exploiting topological properties instead of solely relying on geometrical properties. the Delaunay triangulation is a common out off-the-shelf strategy to triangulate a point-set and it is used by many researchers as the standard way to create the so called data-graph and despite its positive properties, there are also some drawbacks. We are interested in generating a graph with the following properties: the graph is (i) as unique as possible, (ii) and as discriminative as possible regarding the degree distribution. We pose a combinatorial optimization problem (Min-Weight Max-Entropy Problem) to build such a graph by minimizing the total weight cost of the edges and at the same time maximizing the entropy of the degree distribution. Our optimization approach is based on Dynamic Programming (DP) and yields a polynomial time algorithm. © Springer international Publishing Switzerland 2015.

关键词： Combinatorial optimization

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5th international workshop on Computational Forensics, IWCF 2012 and 6th international workshop on Computational Forensics, IWCF 2014

5th International Workshop on Computational Forensics, IWCF ...

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5th international workshop on Computational Forensics, IWCF 2012 and 6th international workshop on Computational Forensics, IWCF 2014

ISBN: (纸本)9783319201245

the proceedings contain 20 papers. the special focus in this conference is on Biometrics, Document Image Inspection and Applications. the topics include: Voice passphrase variability evaluation for speaker recognition;studies in individuality;robust 2d face recognition under different illuminations using binarized partial face features;comparison of multidirectional representations for multispectral palmprint recognition;efficient iris recognition system using relational measures;a study of identification performance of facial regions from CCTV images;inverse of low resolution line halftone images for document inspection;when document security brings new challenges to document analysis;stamp verification for automated document authentication;introducing and analysis of the windows 8 event log for forensic purposes;automatic creation of computer forensic test images;art forgery detection via craquelure pattern matching;forensics acquisition and analysis of instant messaging and VOIP applications and an integrated tool for forensic writer identification.

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Report on the first contest on graph matching algorithms for pattern search in biological databases 10

Report on the first contest on graph matching algorithms for...

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10th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2015

作者： Carletti, Vincenzo Foggia, Pasquale Vento, Mario Jiang, Xiaoyi Department of Information Engineering Electrical Engineering and Applied Mathematics University of Salerno Fisciano Italy Department of Computer Science University of Münster Münster Germany

ISBN: (纸本)9783319182230

graphs are a powerful data structure that can be applied to several problems in bioinformatics, and efficient graph matching is often a tool required for several applications that try to extract useful information from large databases of graphs. While graph matching is in general a NP-complete problem, several algorithms exist that can be fast enough on practical graphs. However, there is no single algorithm that is able to outperform the others on every kind of graphs, and so it is of paramount importance to assess the algorithms on graphs coming from the actual problem domain. To this aim, we have organized the first edition of the Contest on graph Matching Algorithms for pattern Search in Biological Databases, hosted by the ICPR2014 Conference, so as to provide an opportunity for comparing state-of-the-art matching algorithms on a new graph database built using several kinds of real-world graphs found in bioinformatics applications. the participating algorithms were evaluated with respect to both their computation time and their memory usage. this paper will describe the contest task and databases, will provide a brief outline of the participating algorithms, and will present the results of the contest. © Springer international Publishing Switzerland 2015.

关键词： Bioinformatics

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Incremental embedding within a dissimilarity-based framework 10

Incremental embedding within a dissimilarity-based framework

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10th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2015

作者： Hafiane, Rachid Brun, Luc Tabbone, Salvatore Université de Lorraine LORIA-UMR 7503 BP 239 LORIA - Campus scientifique Vandoeuvre-lès-Nancy Cedex54506 France ENSICAEN GREYC UMR CNRS 6072 6 boulevard du Maréchal Juin Caen Cedex14050 France

ISBN: (纸本)9783319182230

Structural pattern recognition methods based on strings or graphs provide a natural encoding of objects’ relationships but can usually be combined only with a few set of machine learning methods. this last decade has seen majors advancements aiming to link these two fields. the two majors research fields in this direction concern the design of new graph and string kernels and different explicit embedding schemes of structural data. Explicit embedding of structural data can be combined with any machine learning methods. Dissimilarity representation methods are important because they allow an explicit embedding and the connection with the kernel framework. However these methods require the whole universe to be known during the learning phase and to obtain a Euclidean embedding, the matrix of dissimilarity encoding dissimilarities between any pair of objects should be regularized. this last point somehow violates the usual separation between training and test sets since both sets should be jointly processed and is an important limitation in many practical applications where the test set is unbounded and unknown during the learning phase. Moreover, requiring the whole universe represents a bottleneck for the processing of massive dataset. In this paper, we propose to overcome these limitations following an incremental embedding based on dissimilarity representations. We study in this paper, the pros and cons of two methods, which allow computing implicitly, and separately the embedding of points in the test set and in the learning set. Conclusions are set following experiments performed on different datasets. © Springer international Publishing Switzerland 2015.

关键词： Signal encoding

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A subpath kernel for learning hierarchical image representations 10

A subpath kernel for learning hierarchical image representat...

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10th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2015

作者： Cui, Yanwei Chapel, Laetitia Lefèvre, Sébastien University Bretagne-Sud Vannes56000 France

ISBN: (纸本)9783319182230

Tree kernels have demonstrated their ability to deal with hierarchical data, as the intrinsic tree structure often plays a discriminative role. While such kernels have been successfully applied to various domains such as nature language processing and bioinformatics, they mostly concentrate on ordered trees and whose nodes are described by symbolic data. Meanwhile, hierarchical representations have gained increasing interest to describe image content. this is particularly true in remote sensing, where such representations allow for revealing different objects of interest at various scales through a tree structure. However, the induced trees are unordered and the nodes are equipped with numerical features. In this paper, we propose a new structured kernel for hierarchical image representations which is built on the concept of subpath kernel. Experimental results on both artificial and remote sensing datasets show that the proposed kernel manages to deal with the hierarchical nature of the data, leading to better classification rates. © Springer international Publishing Switzerland 2015.

关键词： Image classification

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6th international Symposium on Artificial Intelligence supported by the Japanese Society for Artificial Intelligence, JSAI 2014

6th International Symposium on Artificial Intelligence suppo...

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6th international Symposium on Artificial Intelligence, JSAI 2014

ISBN: (纸本)9783662481189

the proceedings contain 26 papers. the special focus in this conference is on Artificial Intelligence supported by the Japanese Society for Artificial Intelligence. the topics include: Codability and robustness in formal natural language semantics;formal analysis of epistemic modalities and conditionals based on logic of belief structures;a type-logical account of quantification in event semantics;towards a wide-coverage tableau method for natural logic;resolving modal anaphora in dependent type semantics;canonical constituents and non-canonical coordination;strict comparison and weak necessity;computing the semantics of plurals and massive entities using many-sorted types;using signatures in type theory to represent situations;scope as syntactic abstraction;focus and givenness across the grammar;eighth international workshop on juris-informatics (JURISIN 2014);classification of precedents by modeling tool for action and epistemic state;legal question answering using ranking SVM and syntactic/semantic similarity;translating simple legal text to formal representations;analyzing reliability change in legal case;workshop on graph-based algorithms for big data and its applications (GABA 2014);anchored alignment problem for rooted labeled trees;central point selection in dimension reduction projection simple-map with binary quantization;mapping kernels between rooted labeled trees beyond ordered trees;finding ambiguous patterns on grammar compressed string and detecting anomalous subgraphs on attributed graphs via parametric flow.

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6th European Conference of the international Federation for Medical and Biological Engineering, MBEC 2014

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6th European Conference of the international Federation for Medical and Biological Engineering, MBEC 2014

ISBN: (纸本)9783319111278

the proceedings contain 255 papers. the special focus in this conference is on Biomedical Signal Processing, Biomedical Imaging, Image Processing and Biosensors. the topics include: Acute mental stress assessment via short term HRV analysis in healthy adults;objective estimation of hearing compensation by means of auditory spectral representations;removing electrocardiogram in EMG signals of low intensity;personalized protocol to select usable movements for myoelectric pattern recognition;human identification by simultaneous recording of acceleration and ECG data;multivariate spatial analysis of cognitive signals based on selected intrinsic mode functions;multiple correspondence analysis applied to EEG attributes;Lempel ziv complexity of EEG in depression;Hilbert-Huang time-frequency analysis of motor imagery EEG data for brain computer interfaces;intelligent system for emotion recognition based on electrodermal activity processing;automatic prediction of vascular events by heart rate variability analysis in hypertensive patients;integration of characteristic values with symbolization in knee movement analysis;a multiple treatments meta-analysis of a response to the first-line treatment of the chronic lymphocytic leukemia;denoising pulse wave signal based on wavelet decomposition;uncertainties in measurement of nonlinear dynamics in heart rate variability;a particle filtering method for muscle tissue segmentation from MRI images;blood vessel segmentation and centerline tracking using local structure analysis;preprocess enhancement of CT image for liver segmentation with region growing algorithm and the automated diagnosis of strabismus using an infrared camera.

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6th international workshop on Machine Learning in Medical Imaging, MLMI 2015 Held in Conjunction with 18th international Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015

6th International Workshop on Machine Learning in Medical Im...

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6th international workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th international Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015

ISBN: (纸本)9783319248875

the proceedings contain 40 papers. the special focus in this conference is on Machine Learning in Medical Imaging. the topics include: Segmentation of right ventricle in cardiac MR images using shape regression;visual saliency based active learning for prostate MRI segmentation;a new image data set and benchmark for cervical dysplasia classification evaluation;machine learning on high dimensional shape data from subcortical brain surfaces;node-based Gaussian graphical model for identifying discriminative brain regions from connectivity graphs;functional-anatomical discriminative regions for rest FMRI characterization;craniomaxillofacial deformity correction via sparse representation in coherent space;hep-2 staining pattern recognition using stacked fisher network for encoding Weber local descriptor;supervoxel classification forests for estimating pairwise image correspondences;non-rigid free-form 2d-3d registration using statistical deformation model;learning and combining image similarities for neonatal brain population studies;deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images;predicting standard-dose pet image from low-dose pet and multimodal MR images using mapping-based sparse representation;boosting convolutional filters with entropy sampling for optic cup and disc image segmentation from fundus images;brain fiber clustering using non-negative kernelized matching pursuit;detecting abnormal cell division patterns in early stage human embryo development;identification of infants at risk for autism using multi-parameter hierarchical white matter connectomes and multi-atlas context forests for knee MR image segmentation.

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HEp-2 staining pattern recognition using stacked fisher network for encoding Weber local descriptor 1

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作者： Han, Xian-Hua Chen, Yen-Wei Xu, Gang Ritsumeikan University 1-1-1 NojiHigashi Kusatsu Shiga525-8577 Japan

ISBN: (数字)9783319248882

ISBN: (纸本)9783319248875

this study addresses the recognition problem of the HEp-2 cell using indirect immunofluorescent (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum. Generally, the method used for IIF analysis remains subjective, and depends too heavily on the experience and expertise of the physician. this study aims to explore an automatic HEp-2 cell recognition system, in which how to extract highly discriminate visual features plays a key role in this recognition application. In order to realize this purpose, our main efforts include: (1) a transformed excitation domain instead of the raw image domain, which is based on the fact that human perception for disguising a pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus;(2) a simple but robust micro-texton without any quantization in the excitation domain, called as Weber local descriptor (WLD);(3) a data-driven coding strategy with a parametric probability process, and the extraction of not only low- but also high-order statistics for image representation called as Fisher vector;(4) the stacking of the Fisher network into deep learning framework for more discriminate feature. Experiments using the open HEp-2 cell dataset released in the ICIP2013 contest validate that the proposed strategy can achieve a much better performance than the state-of-the-art approaches, and that the achieved recognition error rate is even very significantly below the observed intra-laboratory variability. © Springer international Publishing Switzerland 2015.

关键词： Deep learning

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