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IEEE International Conference on Systems, Man and Cybernetics

作者： Shanshan Wang Junhai Zhai Sufang Zhang Hong Zhu School of Computer Science and Technology Hebei University Baoding China Key Lab. of Machine Learning and Computational Intelligence Hebei University Baoding China College of Mathematics Zhejiang Normal University Jinhua China Hebei Branch of Meteorological Cadres Training Institute China Meteorological Administration Baoding China

ISBN: (纸本)9781479986989

Ordinal decision tree (ODT) can effectively deal with monotonic classification problems. However, it is difficult for the existing ordinal decision tree algorithms to learning ODT from large data sets. Based on the variable consistency dominance based rough set approach (VC-DRSA), an ordinal random forest algorithm is proposed in this paper. Combining with the computing framework of MapReduce, the proposed ordinal random forest algorithm is paralleled on the platform of Hadoop, which improves the efficiency of the proposed algorithm. The feasibility and effectiveness of the proposed algorithm is verified by the experimental results.

关键词： Conferences Decision trees Vegetation

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Fast processing of foreign fiber images by image blocking

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Information Processing in Agriculture 2014年第1期1卷 2-13页

作者： Yutao Wu Daoliang Li Zhenbo Li Wenzhu Yang Beijing Engineering&Technology Research Center for Internet of Things in Agriculture China-EU Center for Information and Communication Technologies in Agriculture of China Agriculture UniversityBeijing100083PR China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer ScienceHebei UniversityBaoding071002PR China

In the textile industry,it is always the case that cotton products are constitutive of many types of foreign fibers which affect the overall quality of cotton *** the foundation of the foreign fiber automated inspection,image process exerts a critical impact on the process of foreign fiber *** paper presents a new approach for the fast processing of foreign fiber *** approach includes five main steps,image block,image predecision,image background extraction,image enhancement and segmentation,and image *** first,the captured color images were transformed into gray-scale images;followed by the inversion of gray-scale of the transformed images;then the whole image was divided into several ***,the subsequent step is to judge which image block contains the target foreign fiber image through image *** we segment the image block via OSTU which possibly contains target images after background eradication and image ***,we connect those relevant segmented image blocks to get an intact and clear foreign fiber target *** experimental result shows that this method of segmentation has the advantage of accuracy and speed over the other segmentation *** the other hand,this method also connects the target image that produce fractures therefore getting an intact and clear foreign fiber target image.

关键词： Cotton Foreign fibers Fast image processing Image block Image pre-decision Image connection

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Efficient image representation for object recognition via pivots selection

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Frontiers of computer science 2014年

作者： Xie, Bojun Liu, Yi Zhang, Hui Yu, Jian Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding071000 China

Patch-level features are essential for achieving good performance in computer vision tasks. Besides well-known pre-defined patch-level descriptors such as scaleinvariant feature transform (SIFT) and histogram of oriented gradient (HOG), the kernel descriptor (KD) method [1] offers a new way to "grow-up" features from a match-kernel defined over image patch pairs using kernel principal component analysis (KPCA) and yields impressive *** this paper, we present efficient kernel descriptor (EKD) and efficient hierarchical kernel descriptor (EHKD), which are built upon incomplete Cholesky decomposition. EKD automatically selects a small number of pivot features for generating patch-level features to achieve better computational efficiency. EHKD recursively applies EKD to form image-level features layer-by-layer. Perhaps due to parsimony, we find surprisingly that the EKD and EHKD approaches achieved competitive results on several public datasets compared with other state-of-the-art methods, at an improved efficiency over KD. © 2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

关键词： Object recognition

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Core Set Extreme Learning machine

Core Set Extreme Learning Machine

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作者： Shuxia Lu Bin Liu Caihong Jiao Key Lab.In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University School of Mechanical and Vehicular Engineering Beijing Institute of Technology

A core set extreme learning machine(CSELM) approach is proposed in order to deal with large datasets classification problem. In the first stage, the core set can be obtained efficiently by using the generalized core vector machine(GCVM) algorithm. For the second stage, the extreme learning machine(ELM) can be used to implement classification for much larger datasets. Experiments show that the CSELM has comparable performance with SVM and ELM implementations, but is faster on large datasets.

关键词： Core vector machine Extreme learning machine Support vector machine Core set

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Converting continuous-space language models into N-gram language models for statistical machine translation

Converting continuous-space language models into N-gram lang...

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2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013

作者： Wang, Rui Utiyama, Masao Goto, Isao Sumita, Eiichro Zhao, Hai Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong Unviersity Shanghai200240 China Multilingual Translation Laboratory MASTAR Project National Institute of Information and Communications Technology 3-5 Hikaridai Keihanna Science City Kyoto619-0289 Japan MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong Unviersity Shanghai200240 China

ISBN: (纸本)9781937284978

Neural network language models, or continuous-space language models (CSLMs), have been shown to improve the performance of statistical machine translation (SMT) when they are used for reranking n-best translations. However, CSLMs have not been used in the first pass decoding of SMT, because using CSLMs in decoding takes a lot of time. In contrast, we propose a method for converting CSLMs into back-off n-gram language models (BNLMs) so that we can use converted CSLMs in decoding. We show that they outperform the original BNLMs and are comparable with the traditional use of CSLMs in reranking. © 2013 Association for Computational Linguistics.

关键词： Decoding

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Denoising Diffusion Tensor Images with Shearlet

Denoising Diffusion Tensor Images with Shearlet

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2012 IEEE 11th International Conference on Signal Processing(ICSP 2012)

作者： Xiangfen Zhang Bao-Liang Lu Yan Ma Xiaozhong Xu Fangfang Wei Wenjie Xu Institute of Intelligent Computing & Image Processing College of Mechanical and Electronic EngineeringShanghai Normal University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and EngineeringShanghai Jiaotong University MOE-Microsoft Key Lab for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University

ISBN: (纸本)9781467321969

Diffusion tensor imaging （DTI） is known to be the best non-invasive imaging modality in providing anatomical information as white-matter fiber bundles. However, the Gaussian noise introduced into the diffusion tensor images can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Gaussian noise, many denoising methods have been presented. In this paper, a shearlet based denosing strategy is introduced. To evaluate the efficiency of the proposed shearlet based denoising method in accounting for the Gaussian noise introduced into the images, the peak to peak signal-to-noise ratio （PSNR）, signal-to-mean squared error ratio （SMSE） and edge keeping index （Beta） metrics are adopted. The experiment results acquired from both the synthetic and real data indicate the good performance of our proposed filter.

关键词： diffusion tensor imaging shearlet transform wavelet transform PSNR SMSE denoising

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce 1

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7th IFIP International Conference on Intelligent Information Processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information Processing.

关键词： MapReduce

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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

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13th ACIS International Conference on Software Engineering, Artificial intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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A survey of the initialization of centers and widths in radial basis function network for classification

A survey of the initialization of centers and widths in radi...

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2011 International Conference on machine Learning and Cybernetics, ICMLC 2011

作者： Dong, Chun-Ru Chan, Patrick P.K. Ng, Wing W.Y. Yeung, Daniel S. Machine Learning and Cybernetics Research Center School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

The radial basis function network (RBFN) has been widely used in various fields such as function regression, pattern recognition, and error detection, etc. However, the structural parameters of RBFN including the number of hidden units, centers vectors, and widths (variances) are one of the most importent issues when training a RBFN, which greatly affect the performance of RBFN. So, the objective of this paper is to construct an elementary survey about this problem. Firstly, the fundamental knowledge and notations of RBFN is introduced. Secondly, we summarize most existing network structure initialization methods for RBFN and categorize them into four goups. Then some typical appraoches for each category are introduced and discussed. The disadvantages and virtues for parts of methods are also introduced. Finally, the paper is concluded with a discussion of current difficulties and possible future directions about RBFN architecture selection. © 2011 IEEE.

关键词： Radial basis function networks

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Discretization of continuous-valued attributes in decision tree generation

Discretization of continuous-valued attributes in decision t...

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International Conference on machine Learning and Cybernetics

作者： Li, Wen-Liagn Yu, Rui-Hua Wang, Xi-Zhao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China Department of Information Technology Baoding University Baoding 071000 China

ISBN: (纸本)9781424465262

Decision tree is one of the most popular and widely used classification models in machine learning. The discretization of continuous-valued attributes plays an important role in decision tree generation. In this paper, we improve Fayyad's discretization method which uses the average class entropy of candidate partitions to select boundaries for discretization. Our method can reduce the number of candidate boundaries further. Here we also propose a generalized splitting criterion for cut point selection and prove that the cut points are always on boundaries when using this criterion. Along with the formal proof, we present empirical results that the decision trees generated by using such criteria are similar on several datasets from the UCI machine Learning Repository. © 2010 IEEE.

关键词： Decision trees

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