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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

共 1869 条记录，以下是521-530 订阅

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Energy Minimization Methods in Computer Vision and pattern recognition: 10th international conference,EMMCVPR2015 Hong Kong, China, January 13-16, 2015 Proceedings 10

Energy Minimization Methods in Computer Vision and Pattern R...

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10th international conference on Energy Minimization Methods in Computer Vision and pattern recognition, EMMCVPR 2015

作者： Tai, Xue-Cheng Bae, Egil Chan, Tony F. Lysaker, Marius University of Bergen Department of Mathematics Bergen Norway University of California Department of Mathematics Los AngelesCA United States The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Telemark University College Porsgrunn Norway

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10th iapr-TC-15 international Workshop on Graph-Based Representations in pattern recognition, GbRPR 2015

10th IAPR-TC-15 International Workshop on Graph-Based Repres...

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10th iapr-TC-15 international Workshop on Graph-Based Representations in pattern recognition, GbRPR 2015

ISBN: (纸本)9783319182230

the proceedings contain 36 papers. the special focus in this conference is on Graph-Based Representation, Graph Matching, Graph Clustering and Graph-Based Applications. the topics include: Approximation of graph edit distance in quadratic time;data graph formulation as the minimum-weight maximum-entropy problem;an entropic edge assortativity measure;a subpath kernel for learning hierarchical image representations;coupled-feature hypergraph representation for feature selection;reeb graphs through local binary patterns;incremental embedding within a dissimilarity-based framework;a first step towards exact graph edit distance using bipartite graph matching;consensus of two graph correspondences through a generalisation of the bipartite graph matching;revisiting Volgenant-Jonker for approximating graph edit distance;a hypergraph matching framework for refining multi-source feature correspondences;a tool for solving substitution-tolerant subgraph isomorphism;a graph database repository and performance evaluation metrics for graph edit distance;learning graph model for different dimensions image matching;report on the first contest on graph matching algorithms for pattern search in biological databases;large-scale graph indexing using binary embeddings of node contexts;on the influence of node centralities on graph edit distance for graph classification;a quantum Jensen-Shannon graph kernel using discrete-time quantum walks;density based cluster extension and dominant sets clustering;salient object segmentation from stereoscopic images;causal video segmentation using superseeds and graph matching;fast minimum spanning tree based clustering algorithms on local neighborhood graph and graph based lymphatic vessel wall localisation and tracking.

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Combining ensembles algorithms of symbolic learners 6

Combining ensembles algorithms of symbolic learners

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6th international conference on Information, Intelligence, Systems and Applications (IISA)

作者： Lipitakis, Anastasia-Dimitra Kotsiantis, Sotiris Univ Patras Dept Math Patras Greece

ISBN: (纸本)9781467393119

In this research work an ensemble of bagging, boosting, rotation forest, decorate and random subspace methods with 5 symbolic sub-classifiers in each one is presented. then a voting methodology is used for the final prediction. In order to decrease training time, before building the ensemble redundant features were removed using a slight filter feature selection method. A comparison with simple bagging, boosting, rotation forest, decorate and random subspace methods ensembles with 25 symbolic sub-classifiers is performed, as well as other well-known combining methods, on standard benchmark datasets. the proposed technique is shown to be more accurate than other related methods in most cases.

关键词： data mining machine learning pattern recognition ensembles of classifiers symbolic learner bagging boosting random subspace methods

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Reeb graphs through local binary patterns 10

Reeb graphs through local binary patterns

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10th iapr-TC-15 international Workshop on Graph-Based Representations in pattern recognition, GbRPR 2015

作者： Janusch, Ines Kropatsch, Walter G. Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Vienna Austria

ISBN: (纸本)9783319182230

this paper presents an approach to derive critical points of a shape, the basis of a Reeb graph, using a combination of a medial axis skeleton and features along this skeleton. A Reeb graph captures the topology of a shape. the nodes in the graph represent critical points (positions of change in the topology), while edges represent topological persistence. We present an approach to compute such critical points using Local Binary patterns. For one pixel the Local Binary pattern feature vector is derived comparing this pixel to its neighbouring pixels in an environment of a certain radius. We start with an initial segmentation and a medial axis representation. Along this axis critical points are computed using Local Binary patterns with the radius, defining the neighbouring pixels, set a bit larger than the radius according to the medial axis transformation. Critical points obtained in this way form the node set in a Reeb graph, edges are given through the connectivity of the skeleton. this approach aims at improving the representation of flawed segmented data. In the same way segmentation artefacts, as for example single pixels representing noise, may be corrected based on this analysis. © Springer international Publishing Switzerland 2015.

关键词： Image segmentation

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Intelligent System for Emotion recognition Based on Electrodermal Activity Processing 6

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6th European conference of the international-Federation-for-Medical-and-Biological-Engineering (MBEC)

作者： Bornoiu, Ionut-Vlad Strungaru, Rodica Grigore, Ovidiu Univ Politehn Bucuresti Bd Iuliu Maniu 1-3 Bucharest Romania Univ Politehn Bucuresti Appl Elect & Informat Technol Dept Bucharest Romania

ISBN: (纸本)9783319111278

this paper presents a simple method for detecting a psychological stressed or relaxed state by using only features extracted from the electrodermal activity signal. the signal is acquired with two small electrodes placed on the subject's left hand. Current methodologies used for stress detection provide recognition rates between 40% and 71% while using multiple physiological signals. there are some published papers that describe stress detection methods with satisfactory results without providing a numerical recognition rate. A method used in previous studies to induce a relaxed or stressed state is also presented, which generates similar results in laboratory conditions for different subjects. Also, a very strict signal acquisition protocol is used with the purpose of minimizing artifacts caused by the bad electrode connection, environment or recording device.

关键词： biomedical signal processing electrophysiology pattern recognition stress detection k-nearest neighbors

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12th international Workshop on Multiple Classifier Systems, MCS 2015

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12th international Workshop on Multiple Classifier Systems, MCS 2015

ISBN: (纸本)9783319202471

the proceedings contain 21 papers. the special focus in this conference is on Multiple Classifier Systems. the topics include: A novel bagging ensemble approach for variable ranking and selection for linear regression models;a hierarchical ensemble method for DAG-structured taxonomies;diversity measures and margin criteria in multi-class majority vote ensemble;fractional programming weighted decoding for error-correcting output codes;instance-based decompositions of error correcting output codes;supervised selective combination of diverse object-representation modalities for regression estimation;building classifier ensembles using greedy graph edit distance;measuring the stability of feature selection with applications to ensemble methods;suboptimal graph edit distance based on sorted local assignments;multimodal PLSA for movie genre classification;an experimental study on combining binarization techniques and ensemble methods of decision trees;decision tree-based multiple classifier systems;an empirical investigation on the use of diversity for creation of classifier ensembles and bio-visual fusion for person-independent recognition of pain intensity.

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

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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

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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Network User Interest pattern Mining Based on Entropy Clustering Algorithm 7

Network User Interest Pattern Mining Based on Entropy Cluste...

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6th international conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Xu, Changda Chen, Shuoying Cheng, Jing Beijing Inst Technol Beijing Peoples R China PLA Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781467392006

this paper proposed an automatic clustering algorithm based on entropy for discovering the interest pattern over users' web log. We introduced the information entropy on the basis of clustering algorithm. Compared with traditional clustering algorithms, our method does not require any parameters specified by the end user. Meanwhile, it can discover the clusters in arbitrary shape and size. Experimental results over real-world dataset have fully demonstrated the advantages of our algorithm, which is effective in the problem of high-dimensional and non-informative priors pattern recognition.

关键词： entropy clustering network behavior analysis

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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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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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INCREMENTAL ITERATIVE TIME SPENT BASED RANKING MODEL FOR ONLINE ACTIVITY BASED FRIEND-GROUP RECOMMENDATION SYSTEMS 6

INCREMENTAL ITERATIVE TIME SPENT BASED RANKING MODEL FOR ONL...

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6th international conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Jose, Sachin V. Madhavu, MinuLalitha SBCE Dept CSE Pattoor Alappuzha India

ISBN: (纸本)9781479979837

Online social networks enhance user experience by connecting users with similar interests. Online friend recommendation is a rapid developing field in data mining. Current social networking services prescribe friends to users in light of their social graphs and mutual friends, which may not be the most proper to reflect a user's taste on friend selection in real lifetime. In this paper propose a system that recommends companions based on the daily activities of users. Here a semantic based friend recommendation is done based on the user's life styles such as posting, chatting, searching, commenting etc. By using text mining technique, we display a user's daily life as life archives, from which his/her ways of life are separated by using the Latent Dirichlet Allocation algorithm. At that point we discover a similarity metric to quantify the similarity of life styles between users as an incremental way, and ascertain user effect as far as ways of life with a similarity matching diagram. then calculate user impact ranking iterative matrix vector multiplication strategy in user incrementally, so that it would be versatile to vast scale frameworks. Ranking is mainly based on time spent on activities, profile information and feedback factor. At last, we incorporate a feedback component to further improve the proposal precision.

关键词： activity recognition social networks similarity metric data mining pattern recognition

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