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3rd International Conference on Materials Science and Information Technology, MSIT 2013

作者： Guan, Ming Jie Tian, Xue Dong Yang, Fang Yang, Song Qiang College of Mathematics and Computer Science Hebei University Baoding China Hebei key laboratory of Machine Learning and Computational Intelligence Hebei University China

ISBN: (纸本)9783037857700

It is quite inadequate in providing formula retrieval function by traditional retrieval techniques used in full-text information retrieval system. The main reason is that there are many difficulties to extract the keywords of the mathematical formulas. In this paper, a detailed analysis of the structural characteristics of mathematical formulas and existing index mechanism of mathematical formula searching engine is fulfilled. Then a full-text index (named SLIndex) of mathematical formulas with B+ tree structure is designed and implemented which extracts the structured logic sub-tree feature as keywords of formulas and employs inverted index. Finally, a formula search engine model based on SLIndex is implemented in Apache 2.0 web server. © (2013) Trans Tech Publications, Switzerland.

关键词： Search engines

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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Audio retrieval based on Chinese keyword search in relational databases

Audio retrieval based on Chinese keyword search in relationa...

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3rd International Conference on Materials Science and Information Technology, MSIT 2013

作者： Zhu, Bo Yan Liu, Guang Zhu, Liang College of Art Hebei University Baoding Hebei 071002 China Key Laboratory of Machine Learning and Computational Intelligence School of Mathematics and Computer Science Hebei University Baoding Hebei 071002 China

ISBN: (纸本)9783037857700

In this paper, we propose a new method based on Chinese keyword search to select the WAV or MP3 files in audio post-production. First, we listen to each file and label it with Chinese characters, and then classify and store the files in a relational database system. Then, we use the techniques of Chinese keyword search to match query characters and the tuple characters quickly, and to compute similarities between the query and candidate tuples. For the characteristics of Chinese keyword search, we present a ranking strategy and an algorithm to refine the candidate tuples resulting from the first round matching, and finally get top-N results of audio files. The experimental results show that our method is efficient and effective. © (2013) Trans Tech Publications, Switzerland.

关键词： Relational database systems

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Integrating document clustering and topic modeling

Integrating document clustering and topic modeling

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29th Conference on Uncertainty in Artificial Intelligence, UAI 2013

作者： Xie, Pengtao Xing, Eric P. State Key Laboratory on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 United States

Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local topics specific to each cluster and global topics shared by all clusters. In this paper, we propose a multi-grain clustering topic model (MGCTM) which integrates document clustering and topic modeling into a unified framework and jointly performs the two tasks to achieve the overall best performance. Our model tightly couples two components: a mixture component used for discovering latent groups in document collection and a topic model component used for mining multi-grain topics including local topics specific to each cluster and global topics shared across clusters. We employ variational inference to approximate the posterior of hidden variables and learn model parameters. Experiments on two datasets demonstrate the effectiveness of our model.

关键词： Cluster analysis

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Brain tumor cell density estimation from multi-modal MR images based on a synthetic tumor growth model

Brain tumor cell density estimation from multi-modal MR imag...

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15th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2012

作者： Geremia, Ezequiel Menze, Bjoern H. Prastawa, Marcel Weber, M.-A. Criminisi, Antonio Ayache, Nicholas Asclepios Research Project INRIA Sophia-Antipolis France Computer Science and Artificial Intelligence Laboratory MIT United States Computer Vision Laboratory ETH Zurich Switzerland Scientific Computing and Imaging Institute University of Utah United States Diagnostic and Interventional Radiology Heidelberg University Hospital Germany Machine Learning and Perception Group Microsoft Research Cambridge United Kingdom

ISBN: (纸本)9783642366192

This paper proposes to employ a detailed tumor growth model to synthesize labelled images which can then be used to train an efficient data-driven machine learning tumor predictor. Our MR image synthesis step generates images with both healthy tissues as well as various tumoral tissue types. Subsequently, a discriminative algorithm based on random regression forests is trained on the simulated ground truth to predict the continuous latent tumor cell density, and the discrete tissue class associated with each voxel. The presented method makes use of a large synthetic dataset of 740 simulated cases for training and evaluation. A quantitative evaluation on 14 real clinical cases diagnosed with low-grade gliomas demonstrates tissue class accuracy comparable with state of the art, with added benefit in terms of computational efficiency and the ability to estimate tumor cell density as a latent variable underlying the multimodal image observations. The idea of synthesizing training data to train data-driven learning algorithms can be extended to other applications where expert annotation is lacking or expensive. © 2013 Springer-Verlag.

关键词： Tumors

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An indexing method of mathematical expression retrieval

An indexing method of mathematical expression retrieval

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International Conference on computer Science and Network Technology (ICCSNT)

作者： Xuedong Tian Songqiang Yang Xinfu Li Fang Yang Hebei key laboratory of Machine Learning and Computational Intelligence Baoding China College of Mathematics & Computer Science Hebei University Baoding China

ISBN: (纸本)9781479905607

As the kernel component of scientific documents, mathematical expressions are becoming a new object of searching engines. Different from normal text, mathematical expressions are composed of various kinds of symbols arranged in nonlinear mode, which results in the limitations of traditional full-text information retrieval used for expression searching. In this paper, we discuss the existing search engine of mathematical expressions and introduce the two-dimensional characteristics of mathematical expressions firstly. Then, a data structure of expressing mathematical formulas is designed which contains not only the symbol code but also the mathematical information among symbols. Finally, the indexing algorithm of mathematical expressions is put forward on the basis of the expression data structure. The experimental result shows the effectiveness of the indexing method proposed in this paper.

关键词： Feature extraction Search engines Data mining Indexing Educational institutions Data structures

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Imbalanced extreme support vector machine

Imbalanced extreme support vector machine

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2012 International Conference on machine learning and Cybernetics, ICMLC 2012

作者： Zhou, Xu Lu, Shu-Xia Hu, Li-Sha Zhang, Meng Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781467314855

For the problem of imbalanced data classification which was not discussed in the standard Extreme Support Vector machines (ESVM), an imbalanced extreme support vector machines (IESVM) was proposed. Firstly, a preliminary normal vector of separating hyperplane is obtained directly by geometric analysis. Secondly, penalty factors are obtained which are based on the information provided by data sets projecting onto the preliminary normal vector. Finally, the final separation hyperplane is got through the improved ESVM training. IESVM can overcome disadvantages of traditional designing methods which only consider the imbalance of samples size and can improve the generalization ability of ESVM. Experimental results show that the method can effectively enhance the classification performance on imbalanced data sets. © 2012 IEEE.

关键词： Support vector machines

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Modified AdaBoost based OCSVM ensemble for image retrieval

Modified AdaBoost based OCSVM ensemble for image retrieval

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2012 International Conference on machine learning and Cybernetics, ICMLC 2012

作者： Xing, Hong-Jie Wu, Jian-Guo Chen, Xue-Fang Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China

ISBN: (纸本)9781467314855

For the traditional content-based image retrieval system, the number of irrelevant images for a given query image is significantly more than that of relevant images in an image repository. Therefore, the numbers of negative samples and positive samples are highly unbalanced, which makes the traditional binary classifiers ineffective. In this paper, our proposed modified AdaBoost-based one-class support vector machine (OCSVM) ensemble is utilized to deal with the aforesaid problem. In our proposed method, the weight update formula of training data for AdaBoost is modified to make AdaBoost fit for combining the results of OCSVMs even though OCSVM is regarded as a strong classifier. Compared with the other three related methods, our proposed approach exhibits better performance on the three benchmark image databases. © 2012 IEEE.

关键词： Adaptive boosting

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Selective ensemble of support vector data descriptions for novelty detection

Selective ensemble of support vector data descriptions for n...

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9th International Symposium on Neural Networks, ISNN 2012

作者： Xing, Hong-Jie Chen, Xue-Fang Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China

ISBN: (纸本)9783642313455

Since support vector data description (SVDD) is regarded as a strong classifier, the traditional ensemble methods are not fit for directly combining the results of several SVDDs. Moreover, as is well-known, when many trained classifiers are available, it is better to ensemble some of them rather than all. In this paper, a selective ensemble method based on correntropy is proposed to deal with the foresaid problems. The base classifier used in the proposed ensemble is SVDD. Experimental results on two synthetic data sets and five benchmark data sets demonstrate that the proposed method is superior to its related approaches. © 2012 Springer-Verlag.

关键词： Data description

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Robust feature extraction for novelty detection based on regularized correntropy criterion

Robust feature extraction for novelty detection based on reg...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Huan-Ru Ren Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

ISBN: (纸本)9781467317139

In this paper, a robust feature extraction method based on regularized correntropy criterion (RCC) is proposed for novelty detection. In RCC, the criterion aims to maximize the difference between the correntropy of the normal data with their mean and the correntropy of the novel data with the mean of normal data. Moreover, the optimal projection vectors in the proposed objective function can be obtained by the half-quadratic (HQ) optimization technique with an iterative manner. Experimental results on one synthetic data set and nine benchmark data sets for novelty detection demonstrate that the proposed method is superior to its related approaches.

关键词： Correntropy Half-quadratic optimization Feature extraction Novelty detection Feature extraction Novelty Objective function Remote Control Center Renal Cell Carcinoma Dataset

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