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Vikalpa 2015年第1期40卷 74 - 96页

作者： Janakiraman Moorthy Rangin Lahiri Neelanjan Biswas Dipyaman Sanyal Jayanthi Ranjan Krishnadas Nanath Pulak Ghosh (Coordinator) Director and Professor of Marketing at the Institute of Management Technology Dubai. Earlier he was Professor of Marketing at the IIM Calcutta and IIM Lucknow. He received his PhD from IIM Ahmedabad. His recent research papers were published in the leading scholarly ournals such as Marketing Science British Food Journal Journal of Information Technology Case and Application Research Journal of Database Marketing & Customer Strategy Management. He has wide experience in the banking and investment industry. He was earlier the Global Research and Project Director of the Institute for Customer Relationship Management Atlanta USA. He was the Convener of the prestigious CAT Exam 2011. e-mail: Practice Director leading Atos India's CRM practice while supporting Strategic Business Development for North American Market. With an experience of more than 15 years Rangin has worked extensively as a Business Consultant in Information Technology (Sales Automation Marketing & Service Management area) Customer Data Management and CRM Analytics. e-mail: Business Consultant at Atos with extensive experience in Business Analysis Risk Management Analytics Business Development Presales Solution Ideation on Enterprise Data Management Enterprise Reference Data and Master Data Management area. e-mail: founder and CEO of dono consulting a boutique quantitative analytics and investment research firm. He has worked for leading financial firms in New York and India including Dow Jones Blackstone Sorin Capital (VP Quantitative Modeling) and Thomson Reuters (Head of Real Estate Analytics). A CFA charter holder and Commonwealth Scholar Deep has an MS (Applied Economics) from University of Texas Dallas and an MA (Economics) from Jadavpur University e-mail: Professor in the Information Systems Group of the Institute of Management Technology Ghaziabad. Her PhD is in the field of data mining from Jamia Millia Islamia Central University India. She has published five edited books. She is serving on the editorial b

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BUSINESS ANALYTICS IN THE AGE OF BIG data

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London Business School Review 2014年第3期25卷

作者： Gah‐Yi Vahn Assistant Professor of Management Science and Operations at London Business School. The paper (co‐authored with Cynthia Rudin) mentioned in the article is ‘The Big Data Newsvendor: Practical Insights from Machine Learning’ London Business School Working Paper (available at http://faculty.london.edu/gvahn/research.html)

Gah‐Yi Vahn makes sense of a truly 21st century industry

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Diverse subgroup set discovery

Diverse subgroup set discovery

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作者： Van Leeuwen, Matthijs Knobbe, Arno Machine Learning Department of Computer Science Katholieke Universiteit Leuven Leuven Belgium Algorithmic Data Analysis Department of Information and Computer Sciences Universiteit Utrecht Utrecht Netherlands Leiden Institute of Advanced Computer Science Universiteit Leiden Leiden Netherlands

Large data is challenging for most existing discovery algorithms, for several reasons. First of all, such data leads to enormous hypothesis spaces, making exhaustive search infeasible. Second, many variants of essentially the same pattern exist, due to (numeric) attributes of high cardinality, correlated attributes, and so on. This causes top-k mining algorithms to return highly redundant result sets, while ignoring many potentially interesting results. These problems are particularly apparent with subgroup discovery (SD) and its generalisation, exceptional model mining. To address this, we introduce subgroup set discovery: one should not consider individual subgroups, but sets of subgroups. We consider three degrees of redundancy, and propose corresponding heuristic selection strategies in order to eliminate redundancy. By incorporating these (generic) subgroup selection methods in a beam search, the aim is to improve the balance between exploration and exploitation. The proposed algorithm, dubbed DSSD for diverse subgroup set discovery, is experimentally evaluated and compared to existing approaches. For this, a variety of target types with corresponding datasets and quality measures is used. The subgroup sets that are discovered by the competing methods are evaluated primarily on the following three criteria: (1) diversity in the subgroup covers (exploration), (2) the maximum quality found (exploitation), and (3) runtime. The results show that DSSD outperforms each traditional SD method on all or a (non-empty) subset of these criteria, depending on the specific setting. The more complex the task, the larger the benefit of using our diverse heuristic search turns out to be. © The Author(s) 2012.

关键词： Redundancy

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Support vector novelty detection in hidden space

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Journal of Computational Information Systems 2011年第15期7卷 5581-5590页

作者： Zhang, Li Wang, Bangjun Li, Fanzhang He, Shuping Research Center of Machine Learning and Data Analysis School of Computer Science and Technology Soochow University Suzhou 215006 China

In this paper, a family of support vector novelty detection (or SVND) in hidden space is presented. Firstly a hidden-space SVND (or HSVND) algorithm is proposed. The data in an input space is mapped into a hidden space by using some hidden function. In the hidden space, we can perform the SVND with linear kernel. The advantage of HSVND over SVND is that a hidden function can be an arbitrary real-valued function including Mercer kernels and their combination. In SVND algorithm, only Mercer kernels can be used. Unfortunately, the mapped data may be non-spherical distributed data. In this case, HSVND works badly. Secondly a whitening HSVND (WHSVND) is proposed. We perform whitening method on the mapped data and then the SVND with linear kernel. The whitened data is spherical approximately. We prove that WHSVND is identical to whitening SVND (WSVND) if a single and same Mercer kernel is taken as a mapping function. In fact, above algorithms cannot work well if the data locates a subspace of input sample space or is rank deficiency. In this case, a mapping function only maps data from one subspace to another subspace and cannot make data spanning whole sample space. Thus noised and whitening HSVND (NWHSVND) is present for kernel mapping function. Before the data is mapped, some small randomly noise is put on it. Based on the noised data, the data is mapped into the hidden space. Next we whiten the mapped data, and perform the SVND with linear kernel. Experiments are performed on artificial and real-world data. © 2011 Binary Information Press December, 2011.

关键词： Vectors

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Trace chemical and major constituents measurements of the international space station atmosphere by the vehicle cabin atmosphere monitor

Trace chemical and major constituents measurements of the in...

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42nd International Conference on Environmental Systems 2012, ICES 2012

作者： Darrach, M.R. Chutjian, A. Bornstein, B.J. Croonquist, A.P. Garkanian, V. Hofman, J. Karmon, D. Kenny, J. Kidd, R.D. Lee, S. Macaskill, J.A. Madzunkov, S.M. Mandrake, L. Schaefer, R.T. Toomarian, N. Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109 United States Atomic and Molecular Physics Group JPL/Caltech Pasadena CA 91109 United States Instrument Autonomy Group JPL/Caltech Pasadena CA 91109 United States MicroDevices Group JPL/Caltech Pasadena CA 91109 United States Optical Communications Group JPL/Caltech Pasadena CA 91109 United States Instruments and Science Data Systems JPL/Caltech Pasadena CA 91109 United States Instrument Integration and Test JPL/Caltech Pasadena CA 91109 United States Planetary Chemistry and Astrobiology JPL/Caltech Pasadena CA 91109 United States High Capacity Computing and Modeling JPL/Caltech Pasadena CA 91109 United States Machine Learning and Instrument Autonomy Group JPL/Caltech Pasadena CA 91109 United States Advanced Computer Systems and Technology JPL/Caltech Pasadena CA 91109 United States Advanced Instrument Concepts JPL/Caltech Pasadena CA 91109 United States

ISBN: (纸本)9781600869341

We report on trace gas and major atmospheric constituents results obtained by the Vehicle Cabin Atmosphere Monitor (VCAM) following almost two years of operation aboard the International Space Station (ISS). VCAM isan autonomous environmental monitor based on a highly compact gas chromatograph/quadrupole ion trap mass spectrometer. It was flown to the International Space Station (ISS) on shuttle mission STS-131 and commenced operations on June 2010. VCAM is capable of providing measurements of both parts-per-billion (ppb) levels of volatile trace-gas constituents, and of the atmospheric major constituents (nitrogen, oxygen, argon, and carbon dioxide) in a space vehicle or station. It is designed to operate autonomously and maintenance-free, approximately once per day, with a self-contained gas supply sufficient for a one-year lifetime. VCAM's performance is sufficient to detect and identify 90% of the target compounds at their 180-day Spacecraft Maximum Allowable Concentration levels. © 2012 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Gases

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Cloud-Based Event-Processing Architecture for Opinion Mining

Cloud-Based Event-Processing Architecture for Opinion Mining

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IEEE World Congress on Services (SERVICES)

作者： Stella Gatziu Grivas Marc Schaaf Michael Kaschesky Guillaume Bouchard Information and Knowledge Management Unit University of Applied Science Switzerland E-Government Unit Bern University of Applied Sciences Switzerland Data Mining and Machine Learning Group Xerox Research Centre Europe Grenoble France

The viability of cloud computing for information-intensive tasks arising in real-time opinion mining and sentiment analysis of large online text streams is described. We show how a smart distributed architecture enables an efficient and scalable design for opinion mining on internet-based content that answers key challenges, such as integrating heterogeneous data sources and adapting to events through dynamic system configuration. In particular, we present a novel approach of semantic complex event processing in a cloud environment capturing different levels of information, such as event data (e.g. content from various heterogeneous, distributed sources) as well as associations identified during the opinion mining and sentiment analysis process (e.g. dynamic co-reference resolution).

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Application of SVM in Citation Information Extraction

Application of SVM in Citation Information Extraction

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International Workshop on Complexity and data Mining (IWCDM)

作者： Jiguang Liang Robert Layton Wei Wang Web Data Mining Laboratory Department of Education Technology Educational Science College Nanjing Normal University Nanjing Jiangsu China Internet Commerce Security Laboratory University of Ballarat Ballarat Australia Machine Learning and Cognition Laboratory Department of Education Technology Educational Science College Nanjing Normal University Nanjing Jiangsu China

Support Vector machines are an effective form of binary-class classification algorithm. To enhance the utilization of text structural features for information extraction, which are greatly restricted by the Hidden Markov Model (HMM), this paper proposes a support vector machine multi-class classification based on Markov properties to extract the information from a citation database. The proposed model extracts symbol characteristics as features and composes a binary tree of the transition probabilities. Experiments show that the proposed method outperforms HMM and basic SVM methods.

关键词： Hidden Markov models Support vector machines Feature extraction Binary trees data mining Markov processes Probability

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Using onboard clustering to summarize remotely sensed imagery

Using onboard clustering to summarize remotely sensed imager...

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作者： Hayden, David S. Chien, Steve Thompson, David R. Castano, Rebecca United States Artificial Intelligence Group JPL Australia Machine Learning and Instrument Autonomy Group JPL United States Instrument Software and Science Data Systems Section JPL United States

Many current and future NASA missions are capable of collecting enormous amounts of data, of which only a small portion can be transmitted to Earth. Communications are limited due to distance, visibility constraints, and competing mission downlinks. Long missions and highresolution, multispectral imaging devices easily produce data exceeding the available bandwidth. As an example, the HiRISE camera aboard the Mars Reconnaissance Orbiter produces images of up to 16.4 Gbits in data volume but downlink bandwidth is limited to 6 Mbits per second (Mbps) © 2010 IEEE.

关键词： Bandwidth

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On-orbit measurements of the ISS atmosphere by the vehicle cabin atmosphere monitor

On-orbit measurements of the ISS atmosphere by the vehicle c...

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41st International Conference on Environmental Systems 2011, ICES 2011

作者： Darrach, M.R. Chutjian, A. Bornstein, B.J. Croonquist, A.P. Garkanian, V. Haemmerle, V.R. Hofman, J. Heinrichs, W.M. Karmon, D. Kenny, J. Kidd, R.D. Lee, S. MacAskill, J.A. Madzunkov, S.M. Mandrake, L. Rust, T.M. Schaefer, R.T. Thomas, J.L. Toomarian, N. Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109 United States Atomic and Molecular Physics Group JPL/Caltech United States Instrument Autonomy Group JPL/Caltech United States MicroDevices Group JPL/CalTech United States Optical Communications Group JPL/Caltech United States Processing Algorithms and Calibration Engineering JPL/Caltech United States Inst System Engineering JPL/Caltech United States Instruments and Science Data Systems JPL/Caltech United States Instrument Integration and Test JPL/Caltech United States Planetary Chemistry and Astrobiology JPL/Caltech United States High Capacity Computing and Modeling JPL/Caltech United States Machine Learning and Instrument Autonomy Group JPL/Caltech United States SpaceX 1 Rocket Rd Hawthorne CA 90250 United States Advanced Computer Systems and Technology JPL/Caltech United States Advanced Instrument Concepts JPL/Caltech United States

ISBN: (纸本)9781600869488

We report on trace gas and major atmospheric constituents results obtained by the Vehicle Cabin Atmosphere Monitor (VCAM) during operations aboard the International Space Station (ISS). VCAM is an autonomous environmental monitor based on a miniature gas chromatograph/mass spectrometer. It was flown to the ISS on shuttle mission STS-131 and commenced operations on 6/10/10. VCAM provides measurements of ppb-to-ppm levels of volatile trace-gas constituents, and of the atmospheric major constituents (nitrogen, oxygen, argon, and carbon dioxide) in a space vehicle or station. It is designed to operate autonomously and maintenance-free, approximately once per day, with a self-contained gas supply sufficient for a one-year lifetime. VCAM is designed to detect and identify 90% of the target compounds at their 180-day Spacecraft Maximum Allowable Concentration levels. © 2011 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Gases

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learning invariances with stationary subspace analysis

Learning invariances with stationary subspace analysis

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2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

作者： Meinecke, Frank C. Von Bünau, Paul Kawanabe, Motoaki Müller, Klaus-R. Machine Learning Group Dept. Computer Science TU Berlin Franklinstr. 28/29 10587 Berlin Germany Intelligent Data Analysis Group Fraunhofer FIRST.IDA Kekuléstr. 7 12489 Berlin Germany

ISBN: (纸本)9781424444427

Recently, a novel subspace decomposition method, termed 'Stationary Subspace Analysis' (SSA), has been proposed by Bünau et al. [10]. SSA aims to find a linear projection to a lower dimensional subspace such that the distribution of the projected data does not change over successive epochs or sub-datasets. We show that by modifying the loss function and the optimization procedure we can obtain an algorithm that is both faster and more accurate. We discuss the problem of indeterminacies and provide a lower bound on the number of epochs that is needed. Finally, we show in an experiment with simulated image patches, that SSA can be used favourably in invariance learning. ©2009 IEEE.

关键词： Invariance

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