检索结果-内蒙古大学图书馆

2nd IEEE International Conference on computer and Communications, ICCC 2016

作者： Luo, Qingbin Yang, Guowu She, Kun Li, Xiaoyu Yuqi, Wang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Edward S. Rogers Sr. Department of Electrical Computer Engineering Department University of Toronto TorontoONM5S 3G4 Canada Big Data Research Center and School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China College of Computer Minnan Normal University Zhangzhou China

ISBN: (纸本)9781467390262

In this paper, a novel scheme for quantum homomorphic signature based on coherent states is proposed, which is an effective approach to identity authentication for multiple streams to enhance the security of quantum networks. The state used for signature are arbitrarily close in trace distance to states of O(log2n), if the length of the nodes' messages is n-bit. The scheme analysis shows that the scheme is a quantum group homomorphic signature scheme and the signature is unforgeable. © 2016 IEEE.

关键词： Quantum theory

来源：评论

学校读者我要写书评

暂无评论

Erratum to: Updated measurement of time-dependent -violating observables in decays

引用

The European Physical Journal C 2020年第7期80卷 1-8页

作者： Aaij, R. Beteta, C. Abellán Ackernley, T. Adeva, B. Adinolfi, M. Afsharnia, H. aidala, C. A. aiola, S. Ajaltouni, Z. Akar, S. Albicocco, P. Albrecht, J. Alessio, F. Alexander, M. Albero, A. Alfonso Alkhazov, G. Cartelle, P. Alvarez Alves, A. A. Amato, S. Amhis, Y. An, L. Anderlini, L. Andreassi, G. Andreotti, M. Archilli, F. d’Argent, P. Romeu, J. Arnau Artamonov, A. Artuso, M. Arzymatov, K. Aslanides, E. Atzeni, M. Audurier, B. Bachmann, S. Back, J. J. Baker, S. Balagura, V. Baldini, W. Baranov, A. Barlow, R. J. Barsuk, S. Barter, W. Bartolini, M. Baryshnikov, F. Bassi, G. Batozskaya, V. Batsukh, B. Battig, A. Battista, V. Bay, A. Becker, M. Bedeschi, F. Bediaga, I. Beiter, A. Bel, L. J. Belavin, V. Belin, S. Beliy, N. Bellee, V. Belous, K. Belyaev, I. Ben-Haim, E. Bencivenni, G. Benson, S. Beranek, S. Berezhnoy, A. Bernet, R. Berninghoff, D. Bertholet, E. Bertolin, A. Betancourt, C. Betti, F. Bettler, M. O. Beuzekom, M. van Bezshyiko, Ia. Bhasin, S. Bhom, J. Bieker, M. S. Bifani, S. Billoir, P. Birnkraut, A. Bizzeti, A. Bjørn, M. Blago, M. P. Blake, T. Blanc, F. Blusk, S. Bobulska, D. Bocci, V. Garcia, O. Boente Boettcher, T. Boldyrev, A. Bondar, A. Bondar, N. Borghi, S. Borisyak, M. Borsato, M. Borsuk, J. T. Boubdir, M. Bowcock, T. J. V. Bozzi, C. Braun, S. Rodriguez, A. Brea Brodski, M. Brodzicka, J. Gonzalo, A. Brossa Brundu, D. Buchanan, E. Buonaura, A. Burr, C. Bursche, A. Butter, J. S. Buytaert, J. Byczynski, W. Cadeddu, S. Cai, H. Calabrese, R. Cali, S. Calladine, R. Calvi, M. Gomez, M. Calvo Camboni, A. Campana, P. Perez, D. H. Campora Capriotti, L. Carbone, A. Carboni, G. Cardinale, R. Cardini, A. Carniti, P. Akiba, K. Carvalho Vidal, A. Casais Casse, G. Cattaneo, M. Cavallero, G. Cenci, R. Cerasoli, J. Chapman, M. G. Charles, M. Charpentier, Ph. Chatzikonstantinidis, G. Chefdeville, M. Chekalina, V. Chen, C. Chen, S. Chernov, A. Chitic, S.-G. Chobanova, V. Chrzaszcz, M. Chubykin, A. Ciambrone, P. Cicala, M. F. Vidal, X. Cid Ciezarek, G. Cindolo, F. Clarke, P. E. L. Clemencic, M. Cliff, H. V. Closier, Nikhef National Institute for Subatomic Physics Amsterdam The Netherlands Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool UK Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol UK Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France University of Michigan Ann Arbor USA INFN Sezione di Milano Milan Italy University of Cincinnati Cincinnati USA INFN Laboratori Nazionali di Frascati Frascati Italy Fakultät Physik Technische Universität Dortmund Dortmund Germany European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow UK ICCUB Universitat de Barcelona Barcelona Spain Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Gatchina Russia Imperial College London London UK Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France INFN Sezione di Firenze Firenze Italy Institute of Physics Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland INFN Sezione di Ferrara Ferrara Italy Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI) Protvino Russia Protvino Russia Syracuse University Syracuse USA Yandex School of Data Analysis Moscow Russia INFN Sezione di Cagliari Monserrato Italy Department of Physics University of Warwick Coventry UK School of Physics and Astronomy University of Manchester Manchester UK INFN Sezione di Genova Geneva Italy National University of Science and Technology “MISIS” Moscow Russia INFN Sezione di Pisa Pisa Italy National Center for Nuclear Research (NCBJ) Warsaw Poland Centr

In the Introduction section of the original article [1].

In the Introduction section of the original article [1].

关键词：

来源：评论

学校读者我要写书评

暂无评论

Network slicing for service-oriented networks under resource constraints

arXiv

引用

arXiv 2017年

作者： Zhang, Nan Liu, Ya-Feng Farmanbar, Hamid Chang, Tsung-Hui Hong, Mingyi Luo, Zhi-Quan School of Mathematical Sciences Peking University State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huawei Canada Research Center Ottawa Canada Department of Electrical and Computer Engineering University of Minnesota United States Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Network function virtualization

来源：评论

学校读者我要写书评

暂无评论

Resilient parallel similarity-based reasoning for classifying heterogeneous medical cases in MapReduce

引用

Digital Communications and Networks 2016年第3期2卷 145-150页

作者： Haiyan Yu Jiang Shen Man Xu School of Economics and Management Chongqing University of Posts and Telecommunications Chongqing 400065 China College of Management and Economics Tianjin University Tianjin 300072 China Business School Nankai University Tianjin 300711 China School of Computer Science and Engineering University of Electronic Science and Technology of China 611731. China Big Data Research Center University of Electronic Science and Technology of China 611731 China

Given the exponentially increasing volume of heterogenous medical cases, it is difficult to efficiently perform similarity-based reasoning （SBR） on a centralized machine. In this paper, we investigate how to perform SBR using MapReduce （SBRMR）, which is an inference framework for data-intensive applications over clusters of computers. To combine the similarities from the individual machines, a mixed integer optimization problem is formulated to filter the priority reference cases. Besides, a resilient mapping mechanism is employed using a quadratic optimization model for weighting the attributes and making the neighborhoods in the same class compact, hence improving the inference capacity. Our experiments on classifying the medical cases demonstrate that SBRMR has approximately 4.1% improvement in classification accuracy over SBR, which suggests that SBRMR is an efficient and resilient similarity-based inference approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prompt Λ _c ⁺ production in pPb collisions at √sNN=5.02 TeV

引用

Journal of High Energy Physics 2019年第2期2019卷 1-26页

作者： Aaij, R. Adeva, B. Adinolfi, M. aidala, C.A. Ajaltouni, Z. Akar, S. Albicocco, P. Albrecht, J. Alessio, F. Alexander, M. Alfonso Albero, A. Ali, S. Alkhazov, G. Alvarez Cartelle, P. Alves, A.A. Amato, S. Amerio, S. Amhis, Y. An, L. Anderlini, L. Andreassi, G. Andreotti, M. Andrews, J.E. Appleby, R.B. Archilli, F. d’Argent, P. Arnau Romeu, J. Artamonov, A. Artuso, M. Arzymatov, K. Aslanides, E. Atzeni, M. Audurier, B. Bachmann, S. Back, J.J. Baker, S. Balagura, V. Baldini, W. Baranov, A. Barlow, R.J. Barsuk, S. Barter, W. Baryshnikov, F. Batozskaya, V. Batsukh, B. Battista, V. Bay, A. Beddow, J. Bedeschi, F. Bediaga, I. Beiter, A. Bel, L.J. Beliy, N. Bellee, V. Belloli, N. Belous, K. Belyaev, I. Ben-Haim, E. Bencivenni, G. Benson, S. Beranek, S. Berezhnoy, A. Bernet, R. Berninghoff, D. Bertholet, E. Bertolin, A. Betancourt, C. Betti, F. Bettler, M.O. van Beuzekom, M. Bezshyiko, Ia. Bhasin, S. Bhom, J. Bifani, S. Billoir, P. Birnkraut, A. Bizzeti, A. Bjørn, M. Blago, M.P. Blake, T. Blanc, F. Blusk, S. Bobulska, D. Bocci, V. Boente Garcia, O. Boettcher, T. Bondar, A. Bondar, N. Borghi, S. Borisyak, M. Borsato, M. Bossu, F. Boubdir, M. Bowcock, T.J.V. Bozzi, C. Braun, S. Brodski, M. Brodzicka, J. Brossa Gonzalo, A. Brundu, D. Buchanan, E. Buonaura, A. Burr, C. Bursche, A. Buytaert, J. Byczynski, W. Cadeddu, S. Cai, H. Calabrese, R. Calladine, R. Calvi, M. Calvo Gomez, M. Camboni, A. Campana, P. Campora Perez, D.H. Capriotti, L. Carbone, A. Carboni, G. Cardinale, R. Cardini, A. Carniti, P. Carson, L. Carvalho Akiba, K. Casse, G. Cassina, L. Cattaneo, M. Cavallero, G. Cenci, R. Chamont, D. Chapman, M.G. Charles, M. Charpentier, Ph. Chatzikonstantinidis, G. Chefdeville, M. Chekalina, V. Chen, C. Chen, S. Chitic, S.-G. Chobanova, V. Chrzaszcz, M. Chubykin, A. Ciambrone, P. Cid Vidal, X. Ciezarek, G. Clarke, P.E.L. Clemencic, M. Cliff, H.V. Closier, J. Coco, V. Coelho, J.A.B. Cogan, J. Cogneras, E. Cojocariu, L. Collins, P. Colombo, T. Comerma-Montells, A. Contu, A. Coombs, G. Coquereau, S. Corti, G. Corvo, M. Costa Sobra Centro Brasileiro de Pesquisas Físicas (CBPF) Rio de Janeiro Brazil Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Center for High Energy Physics Tsinghua University Beijing China Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS IN2P3-LAPP Annecy France Clermont Université Université Blaise Pascal CNRS/IN2P3 LPC Clermont-Ferrand France Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Orsay France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Max-Planck-Institut für Kernphysik (MPIK) Heidelberg Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany School of Physics University College Dublin Dublin Ireland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano-Bicocca Milano Italy INFN Sezione di Milano Milano Italy INFN Sezione di Cagliari Monserrato Italy INFN Sezione di Padova Padova Italy INFN Sezione di Pisa Pisa Italy INFN Sezione di Roma Tor Vergata Roma Italy INFN Sezione di Roma La Sapienza Roma Italy Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Nikhef National Institute for Subatomic Physics and VU University Amsterdam Amsterdam Netherlands Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences Kraków Poland AGH - University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland National Center for Nuclear Research (NCBJ) Warsaw Poland Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest-Magurele Romania Petersburg Nuclear Physics Institute

The prompt production of Λ c + baryons is studied in proton-lead collisions collected with the LHCb detector at the LHC. The data sample corresponds to an integrated luminosity of 1.58 nb −1 recorded at a nucleon-nuc... 详细信息

The prompt production of Λ _c ⁺ baryons is studied in proton-lead collisions collected with the LHCb detector at the LHC. The data sample corresponds to an integrated luminosity of 1.58 nb ⁻¹ recorded at a nucleon-nucleon centre-of-mass energy of sNN=5.02 TeV. Measurements of the differential cross-section and the forwardbackward production ratio are reported for Λ _c ⁺ baryons with transverse momenta in the range 2 < p _T < 10 GeV/c and rapidities in the ranges 1.5 < y ^∗ < 4.0 and −4.5 < y ^∗ < −2.5 in the nucleon-nucleon centre-of-mass system. The ratio of cross-sections of Λ _c ⁺ baryons and D ⁰ mesons is also reported. The results are compared with next-to-leading order calculations that use nuclear parton distribution functions.[Figure not available: see fulltext.] © 2019, The Author(s).

关键词： Charm physics Forward physics Heavy Ion Experiments Heavy quark production

来源：评论

学校读者我要写书评

暂无评论

Automatic Matching Release Notes and Source Code by Generating Summary for Software Change 6

Automatic Matching Release Notes and Source Code by Generati...

引用

6th International Conference on Digital Home, ICDH 2016

作者： Huang, Yuan Liu, Zhiyong Chen, Xiangping Luo, Xiaonan School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China National Engineering Research Center of Digital Life Guangzhou510006 China Institute of Advanced Technology Sun Yat-sen University Guangzhou510006 China

ISBN: (纸本)9781509044009

To quickly locate the source code that maps to a specific change described in change history, establishing traceability links between release notes and source code is a necessary task. Current works on the traceability link recovery can be used to find out source code changes which are of higher textual similarities with the release note. However, these approaches rely on consistency of the text used in artifacts at various abstraction levels, and the completeness of text descriptions. In this paper, we propose to leverage source code change information for improving the accuracy of release note to source code traceability recovery tasks. In order to reduce the complexity of link recovery, our approach first performs change impact analysis to cluster the source code changes for the same purpose as a virtual class. After that, our approach employs a natural language generation algorithm to generate readable summary sentence for each virtual class. The traceability links are built between release notes and clusters of program entities by computing the linguistic similarity of sentences. We conduct case studies on 26 releases of 3 popular softwares to evaluate the approach, and the results indicate that our proposed method can improve the accuracy of traceability link recovery compared to other IR-based techniques. © 2016 IEEE.

关键词： Recovery

来源：评论

学校读者我要写书评

暂无评论

Mean-field theory for the Nagel-Schreckenberg model with overtaking strategy

引用

Journal of Physics: Conference Series 2018年第1期1113卷

作者： Zhu Su Weibing Deng Jihui Han Wei Li Xu Cai National Engineering Laboratory for Technology of Big Data Applications in Education Central China Normal University Wuhan P. R. China Complexity Science Center Institute of Particle Physics Central China Normal University Wuhan P. R. China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou P.R. China

Based on the Nagel-Schreckenberg (NS) model with periodic boundary conditions, a modified model considered overtaking strategy (NSOS) has been proposed [1, 2]. In this paper, we focus on the theoretical analysis of traffic flow for NSOS model by using mean-field method. In the special case of vmax = 1 where vehicles can not overtake preceding ones, the features of stationary state can be obtained exactly. However, in the case of vmax > 1 where overtaking happens, some approximative methods have to be took into account. The main results are that we find the reason why traffic flow is increased in the regime where densities exceed the maximum flow density, and the influence of traffic flow on the transition density is dominated by the braking probability p.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Robust Feature Set for Wearable Multichannel Myoelectric Devices in Practice 6

A Robust Feature Set for Wearable Multichannel Myoelectric D...

引用

6th International Conference on Digital Home, ICDH 2016

作者： Lu, Huiyang Zhang, Haoshi Lin, Mouguang Wang, Ruomei School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China National Engineering Research Center of Digital Life Guangzhou510006 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781509044009

A wearable myoelectric device is essentially a surface electromyography(sEMG) based human machine interaction (HMI) system. The non-stationary property of sEMG could be one of the obstacles that degrade the user experience of wearable myoelectric devices because they need to be put on and taken off frequently, which brings in the time-variation effects specified in this paper. In order to reduce the influence cause by time-variation on wearable myoelectric devices, the common spatial pattern(CSP) was employed to extract relatively robust features from multichannel sEMG signals. As comparation, the conventional time domain(TD) features together with time domain auto regressive(TDAR) features were investigated. To implement the experiment, 16 electrodes were utilized to simulate a wearable myoelectric device, and an absolute value based Teager-Kaiser energy operator(TKEO) strategy was proposed to realize onset detection. The experiment was divided into 2 sessions, corresponding to the situations before and after time-variation happened respectively. The support vector machine (SVM) and linear discrimination analysis(LDA) were used to build the pattern recognition models. In session2, the average classification accuracy of CSP(95.2±2.3%) is superior to TD(90.3±4.4%) and TDAR(92.4±4.2%) under LDA. The result suggests that the CSP feature set is relatively robust against accuracy reduction resulted from time-variation in motion recognition. © 2016 IEEE.

关键词： Electromyography

来源：评论

学校读者我要写书评

暂无评论

Journal of Open Source Software (JOSS): Design and first-year review

arXiv

引用

arXiv 2017年

作者： Smith, Arfon M. Niemeyer, Kyle E. Katz, Daniel S. Barba, Lorena A. Githinji, George Gymrek, Melissa Huff, Kathryn D. Madan, Christopher R. Mayes, Abigail Cabunoc Moerman, Kevin M. Prins, Pjotr Ram, Karthik Rokem, Ariel Teal, Tracy K. Guimera, Roman Valls Vanderplas, Jacob T. Data Science Mission Office Space Telescope Science Institute BaltimoreMD United States School of Mechanical Industrial and Manufacturing Engineering Oregon State University CorvallisOR United States National Center for Supercomputing Applications & Department of Computer Science Department of Electrical and Computer Engineering School of Information Sciences University of Illinois at Urbana–Champaign UrbanaIL United States Department of Mechanical & Aerospace Engineering George Washington University WashingtonDC United States KEMRI–Wellcome Trust Research Programme Kilifi Kenya Departments of Medicine & Computer Science and Engineering University of California San DiegoCA United States Department of Nuclear Plasma and Radiological Engineering University of Illinois at Urbana–Champaign UrbanaIL United States School of Psychology University of Nottingham Nottingham United Kingdom Mozilla Foundation TorontoON Canada MIT Media Lab Massachusetts Institute of Technology CambridgeMA United States Trinity Centre for Bioengineering Trinity College University of Dublin Dublin Ireland University of Tennessee Health Science Center MemphisTN United States University Medical Centre Utrecht Utrecht Netherlands Berkeley Institute for Data Science University of California Berkeley BerkeleyCA United States eScience Institute University of Washington SeattleWA United States Data Carpentry DavisCA United States University of Melbourne Centre for Cancer Research University of Melbourne Melbourne Australia

This article describes the motivation, design, and progress of the Journal of Open Source Software (JOSS). JOSS is a free and open-access journal that publishes articles describing research software. It has the dual goals of improving the quality of the software submitted and providing a mechanism for research software developers to receive credit. While designed to work within the current merit system of science, JOSS addresses the dearth of rewards for key contributions to science made in the form of software. JOSS publishes articles that encapsulate scholarship contained in the software itself, and its rigorous peer review targets the software components: functionality, documentation, tests, continuous integration, and the license. A JOSS article contains an abstract describing the purpose and functionality of the software, references, and a link to the software archive. The article is the entry point of a JOSS submission, which encompasses the full set of software artifacts. Submission and review proceed in the open, on GitHub. Editors, reviewers, and authors work collaboratively and openly. Unlike other journals, JOSS does not reject articles requiring major revision;while not yet accepted, articles remain visible and under review until the authors make adequate changes (or withdraw, if unable to meet requirements). Once an article is accepted, JOSS gives it a digital object identifier (DOI), deposits its metadata in Crossref, and the article can begin collecting citations on indexers like Google Scholar and other services. Authors retain copyright of their JOSS article, releasing it under a Creative Commons Attribution 4.0 International License. In its first year, starting in May 2016, JOSS published 111 articles, with more than 40 additional articles under review. JOSS is a sponsored project of the nonprofit organization NumFOCUS and is an affiliate of the Open Source Initiative (OSI). Copyright © 2017, The Authors. All rights reserved.

关键词： Abstracting

来源：评论

学校读者我要写书评

暂无评论

A Proportion Learning Algorithms with Density Peaks

引用

Procedia computer science 2016年 91卷 841-846页

作者： Limeng Cui Zhiquan Qi Fan Meng School of Computer and Control Engineering UCAS Beijing China Research Center on Fictitious Economy & Data Science CAS Beijing China School of Economics and Management UCAS Beijing China

As a powerful tool of weakly labeled learning, proportion learning has drawn much attention in recent years. In this problem, instances are grouped in bags and only the proportion of labels is known in each bag. It has broad applications, such as video event detection and the prediction of political election. However, proportion learning problem has suffered great challenge because of the sensitivity to the distribution of data and complexity of the iterative optimization procedure. To overcome these drawbacks, we propose a novel proportion learning method based on the distribution information of data. We demonstrate the power of our algorithm on several test cases. In addition, our method is fast and robust to the shape of data.

关键词： proportion learning clustering density peaks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：