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A fuzzy support vector machine algorithm with dual membership based on hypersphere

Journal of Computational information Systems 2011年第6期7卷 2028-2034页

作者： Ding, Shifei Gu, Yaxiang School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China

In traditional fuzzy support vector machine(FSVM), membership function is established in global scope will reduce the membership of support vectors, and the FSVM based dismissing margin increases the training speed, but will remove some support vector artificially. So, a new algorithm of Fuzzy Support Vector Machine with Dual Membership based on Hypersphere (HDM-FSVM) is proposed. In this algorithm, the two classes of hyperspheres are divided into two parts respectively. Then, according to most support vectors are in the hemispheres which close together, we use the membership function that can enhance the membership of support vector, and because of there are a few of support vectors in other hemispheres, we must ensure the high membership of support vectors and reduce the membership of non-support vector. In order to removal noise and outliers, we introduce a radius controlling factor to control size of hyperspheres, the samples that outside of hyperspheres are considered as noise and outliers. Experimental results show that HDM-FSVM can enhance the classification accuracy rate of the sample sets that contain noise and outliers. Copyright © 2011 Binary information Press.

关键词： Membership functions

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A Novel Dependency-to-String Model for Statistical Machine Translation 11

A Novel Dependency-to-String Model for Statistical Machine T...

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Conference on Empirical Methods in Natural Language Processing

作者： Jun Xie Haitao Mi Qun Liu Key Laboratory of Intelligent Information Processiong Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)9781618391100

Dependency structure, as a first step towards semantics, is believed to be helpful to improve translation quality. However, previous works on dependency structure based models typically resort to insertion operations to complete translations, which make it difficult to specify ordering information in translation rules. In our model of this paper, we handle this problem by directly specifying the ordering information in head-dependents rules which represent the source side as head-dependents relations and the target side as strings. The head-dependents rules require only substitution operation, thus our model requires no heuristics or separate ordering models of the previous works to control the word order of translations. Large-scale experiments show that our model performs well on long distance reordering, and outperforms the state-of-the-art constituency-to-string model (+1.47 BLEU on average) and hierarchical phrase-based model (+0.46 BLEU on average) on two Chinese-English NIST test sets without resort to phrases or parse forest. For the first time, a source dependency structure based model catches up with and surpasses the state-of-the-art translation models.

关键词： machine translation Resorts Belgo-Luxembourg Economic Union reduced mass Translations BASES(STRUCTURES) Semantics Heuristics

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DH-TRIE frequent pattern mining on Hadoop using JPA

DH-TRIE frequent pattern mining on Hadoop using JPA

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2011 IEEE International Conference on Granular computing, GrC 2011

作者： Yang, Lai Shi, Zhongzhi Xu, Li D. Liang, Fan Kirsh, Ilan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China Dept. of ITDS Old Dominion University Norfolk VA 23529 United States Academic College of Tel-Aviv Yaffo Israel

ISBN: (纸本)9781457703713

The FPgrowth is a famous frequent pattern's algorithm in data mining when working with high-dimensional, large-scale data sets. It is also known as great complexity on memory for the recursively processing. In general, FPgrowth cannot handle large-scale data set unless dividing a whole data set into small blocks. Based on Hadoop, the open cloud computing model, a distributed DH-TRIE frequent pattern algorithm using JPA is proposed, which solved the three problems (globalization, random-write and duration). The algorithm is shown good flexibility and scalability by comparisons to mahout project. By applied to a virtualization platform Vega Cloud, the algorithm will be used in far-ranging situations. © 2011 IEEE.

关键词： Cloud computing

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Fault pattern classification using modified dempster-shafer(D-S) combination rule

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Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis 2011年第2期31卷 144-149页

作者： Su, Xiaoyan Deng, Yong Wu, Ying Jiang, Wen School of Electronics Information and Electric Engineering Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of Digital Agricultural Early-Warning Technology of Ministry of Agriculture Beijing 100081 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan 411105 China College of Computer and Information Sciences Southwest University Chongqing 400715 China School of Electronics and Information Technology Northwestern Polytechnical University Xi'an 710072 China

A modified method of conventional D-S evidence combination rule was presented. The consensus index of evidence based on distance measures of evidence was introduced into modifying the BPAs of hypotheses with the evidence sufficiency and evidence importance. By using the D-S combination and new decision rules, the different sets of BPAs for each hypothesis were calculated. A numerical example of the fault diagnosis is used to demonstrate the validity of the method by comparison with other methods.

关键词： Failure analysis

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Fast common visual pattern detection via radiate geometric model

Fast common visual pattern detection via radiate geometric m...

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IEEE International Conference on Image Processing

作者： Lingyang Chu Shuqiang Jiang Qingming Huang Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences China Institute of Computing Technology CAS Beijing China Chinese Academy of Sciences Beijing China

In this paper, we propose a novel method to implement fast detection of Common Visual Pattern (CVP). The purpose of CVP detection is to find the correspondences between the common visual regions of two given partial duplicate images. There are two major components of the proposed method which guarantee the good performance. First, we establish the Radiate-Geometric-Model (RGM). The RGM is represented by a set of radiate structures, and each structure is geometrically made up of a group of matched feature pairs. By utilizing the statistical information gained from the radiate structures, the RGM can not only quickly estimate the potential pairs of common regions but also organize the scale relationship between matched pairs into a compact form, hence increase the detection speed substantially. Second, we formulize the Radiate-Geometric-Model (RGM) into a graph optimization problem which could be solved by the method of graph-shift, thus make our algorithm capable of detecting the CVPs of all kinds of correspondences. Experimental results prove that the speed of our algorithm is at least 40 times faster than the state-of-the-art, while achieving a better detection performance at the same time.

关键词： Accuracy Histograms Visualization Optimization Feature extraction Conferences Vectors

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GPU based fast algorithm for tanner graph based image interpolation

GPU based fast algorithm for tanner graph based image interp...

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IEEE Workshop on Multimedia Signal Processing

作者： Wei Lei Ruiqin Xiong Siwei Ma Luhong Liang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China Institute of Digital Media Peking University Beijing China

In image/video processing software and hardware products, low complexity interpolation algorithms, such as cubic and splines methods, are commonly used. However, these methods tend to blur textures and produce jaggy effect compared with other adaptive methods such as NEDI, SAI. Tanner graph based image interpolation algorithm has better effect in dealing with edge and texture, but with high computation complexity. Thanks to the high performance parallel processing capability of today's GPU, use of complex algorithms for real time application is becoming possible. In this paper, we present a fast algorithm for tanner graph based image interpolation and it's implementation on GPU. In our algorithm, the image model training process of tanner graph based image interpolation is greatly simplified. Experimental results show that the GPU implementation can be more than 47 times as fast as the CPU implementation.

关键词： Graphics processing unit Interpolation Training Instruction sets Parallel processing Equations Image resolution

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MST based stream data clustering algorithm

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2011年第9期5卷 352-360页

作者： Cheng, Zheng Xiaodong, Zhu Jingcheng, Li Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Anhui University China School of Computer Science and Technology Anhui University China Institute of Information Management and Electronic Business University of Shanghai for Science and Technology China

How to make use of limited memory space and processing speeds of computer for rapid and accurate data mining has become an important research topic on the stream data cluster analysis. A stream data clustering algorithm based on the minimum spanning tree (MSTSC) is described. MSTSC is divided into online processing and offline clustering. Stream data are analyzed online by using two groups of processing unit respectively. In offline process clusters is taken as representative objects, and the minimum spanning tree algorithm is used in offline clustering. MSTSC can improve the clustering quality on non-spherical clusters. Some experiments are carried out in both real data sets and synthetic data sets. Results show that MSTSC algorithm not only can deal with non-spherical clusters effectively, but also has better efficiency and clustering quality. In addition, MSTSC is insensitive to order of input data, and has a good effect for skewed class distributions.

关键词： Cluster analysis

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A novel coarse-to-fine hair segmentation method

A novel coarse-to-fine hair segmentation method

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International Conference on Automatic Face and Gesture Recognition

作者： Dan Wang Xiujuan Chai Hongming Zhang Hong Chang Wei Zeng Shiguang Shan Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China NEC Laboratories Beijing China

Segmenting hair regions from human images facilitates many tasks like hair synthesis and hair style trends forecast. However, hair segmentation is quite challenging due to hair/background confusion and large hair pattern diversity. To address these problems to some extent, this paper proposes a novel coarse-to-fine hair segmentation method. In our approach, firstly, the recently proposed “Active Segmentation with Fixation” (ASF) is used to coarsely define an enclosed candidate region with high-recall (but possibly low-precision) of hair pixels and exclude considerable part of the backgrounds which are easily confused with hair. Then Graph Cuts (GC) method is applied to the candidate regions to remove additional false positives by incorporating hair-specific information. Specifically, Bayesian method is employed to select some reliable hair and background regions (seeds) among the ones over-segmented by Mean Shift. SVM classifier is then learnt online from these seeds and explored to predict hair/background likelihood probability, which is subsequently fed into GC algorithm. The novelty of the proposed approach lies in three folds: 1) an elaborate design of hair segmentation framework, which utilizes ASF to reduce the candidate hair regions and adopts GC to achieve more accurate hair region contours; 2) the region-based strategy for seed selection; 3) the exploration of the discriminative method, SVM, to predict the probability of each pixel belonging to hair and background regions. Extensive experimental results demonstrate the approach outperforms recently proposed methods.

关键词： Hair Pixel Image segmentation Support vector machines Bayesian methods Image color analysis Databases

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Multi-kernel Co-SVM with a new strategy using unlabeled data for image retrieval

Multi-kernel Co-SVM with a new strategy using unlabeled data...

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International Conference on Pervasive computing and Applications (ICPCA)

作者： Li Zongmin Liu Yang Li Hua School of Computer and Communications Engineering China University of Petroleum Shandong China Key Laboratory Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781457702099

Relevance feedback based on SVM classifier shows a good performance recently but the finite feedback counts limited by user's patience and the small sample size problem are not solved well, Co-SVM does a good job in solving these problems but still has some flaws. We propose three strategies to try to improve this algorithm: (1) different kernel functions are used to characterize the color and texture visual similarities; (2) a new method is proposed to caculate the confident scores of the contention samples; (3) a bunch of the most irrelevant images with the highest confident score are added into the labeled images to extend the size of labeled data while choosing a bunch of images for user labeling. Experimental results verify the superiority of our method over Co-SVM.

关键词： Support vector machines Multimedia communication Classification algorithms

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Visual saliency detection by spatially weighted dissimilarity

Visual saliency detection by spatially weighted dissimilarit...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lijuan Duan Chunpeng Wu Jun Miao Laiyun Qing Yu Fu College of Computer Science and Technology Beijing University of Technology Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China School of Information Science and Engineering Chinese Academy and Sciences Beijing China Department of Computing University of Surrey Guildford Surrey UK

In this paper, a new visual saliency detection method is proposed based on the spatially weighted dissimilarity. We measured the saliency by integrating three elements as follows: the dissimilarities between image patches, which were evaluated in the reduced dimensional space, the spatial distance between image patches and the central bias. The dissimilarities were inversely weighted based on the corresponding spatial distance. A weighting mechanism, indicating a bias for human fixations to the center of the image, was employed. The principal component analysis (PCA) was the dimension reducing method used in our system. We extracted the principal components (PCs) by sampling the patches from the current image. Our method was compared with four saliency detection approaches using three image datasets. Experimental results show that our method outperforms current state-of-the-art methods on predicting human fixations.

关键词： Humans Color Visualization Image color analysis Principal component analysis Correlation Computational modeling

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