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Proceedings of the IEEE

Issue 1 • Jan. 2016

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Displaying Results 1 - 20 of 20
  • Front cover

    Publication Year: 2016, Page(s): C1
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  • Proceedings of the IEEE publication information

    Publication Year: 2016, Page(s): C2
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  • Table of Contents [Big Data: Theoretical Aspects]

    Publication Year: 2016, Page(s): 1
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  • Solving Puzzles With Missing Pieces: The Power of Systems Biology

    Publication Year: 2016, Page(s):2 - 7
    Cited by:  Papers (2)
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  • Big Data: Theoretical Aspects [Scanning the Issue]

    Publication Year: 2016, Page(s):8 - 10
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  • A Review of Relational Machine Learning for Knowledge Graphs

    Publication Year: 2016, Page(s):11 - 33
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2314 KB) | HTML iconHTML

    Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally differ... View full abstract»

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  • Learning to Hash for Indexing Big Data—A Survey

    Publication Year: 2016, Page(s):34 - 57
    Cited by:  Papers (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1906 KB) | HTML iconHTML

    The explosive growth in Big Data has attracted much attention in designing efficient indexing and search methods recently. In many critical applications such as large-scale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem. However, the straightforward solution using exhaustive comparison is infeasible due to the prohibitive computational compl... View full abstract»

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  • Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments

    Publication Year: 2016, Page(s):58 - 92
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2680 KB) | HTML iconHTML

    In this era of large-scale data, distributed systems built on top of clusters of commodity hardware provide cheap and reliable storage and scalable processing of massive data. With cheap storage, instead of storing only currently relevant data, it is common to store as much data as possible, hoping that its value can be extracted later. In this way, exabytes (1018 bytes) of data are being created ... View full abstract»

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  • Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining

    Publication Year: 2016, Page(s):93 - 110
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1208 KB) | HTML iconHTML

    When can reliable inference be drawn in the “Big Data” context? This paper presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large-scale inference. In large-scale data applications like genomics, connectomics, and eco-informatics, the data set is often variable rich but sample starved: a regime where the n... View full abstract»

    Open Access
  • Resource Allocation for Statistical Estimation

    Publication Year: 2016, Page(s):111 - 125
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (429 KB) | HTML iconHTML

    Statistical estimation in many contemporary settings involves the acquisition, analysis, and aggregation of data sets from multiple sources, which can have significant differences in character and in value. Due to these variations, the effectiveness of employing a given resource, e.g., a sensing device or computing power, for gathering or processing data from a particular source depends on the nat... View full abstract»

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  • Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data

    Publication Year: 2016, Page(s):126 - 135
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (595 KB) | HTML iconHTML

    Large-scale data analysis poses both statistical and computational problems which need to be addressed simultaneously. A solution is often straightforward if the data are homogeneous: one can use classical ideas of subsampling and mean aggregation to get a computationally efficient solution with acceptable statistical accuracy, where the aggregation step simply averages the results obtained on dis... View full abstract»

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  • Learning Reductions That Really Work

    Publication Year: 2016, Page(s):136 - 147
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (612 KB) | HTML iconHTML

    In this paper, we provide a summary of the mathematical and computational techniques that have enabled learning reductions to effectively address a wide class of tasks, and show that this approach to solving machine learning problems can be broadly useful. Our work is instantiated and tested in a machine learning library, Vowpal Wabbit, to prove that the techniques discussed here are fully viable ... View full abstract»

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  • Taking the Human Out of the Loop: A Review of Bayesian Optimization

    Publication Year: 2016, Page(s):148 - 175
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1409 KB) | HTML iconHTML

    Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve m... View full abstract»

    Open Access
  • Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets

    Publication Year: 2016, Page(s):176 - 197
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2848 KB) | HTML iconHTML

    In this paper, we provide an introduction to machine learning tasks that address important problems in genomic medicine. One of the goals of genomic medicine is to determine how variations in the DNA of individuals can affect the risk of different diseases, and to find causal explanations so that targeted therapies can be designed. Here we focus on how machine learning can help to model the relati... View full abstract»

    Open Access
  • Georg Simon Ohm and the First Comprehensive Theory of Electrical Conductivity in Metals [Scanning our Past]

    Publication Year: 2016, Page(s):198 - 209
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (853 KB) | HTML iconHTML

    This month's article focuses on the life and work of Georg Simon Ohm, an experimental physicist who is most well known for establishing Ohm's law. View full abstract»

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  • Future Special Issues/Special Sections of the Proceedings

    Publication Year: 2016, Page(s): 210
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  • Introducing IEEE Collabratec

    Publication Year: 2016, Page(s): 211
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  • IEEE membership can help you reach your personal goals

    Publication Year: 2016, Page(s): 212
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  • IEEE Global History Network

    Publication Year: 2016, Page(s): C3
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  • Back cover

    Publication Year: 2016, Page(s): C4
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Aims & Scope

The most highly-cited general interest journal in electrical engineering and computer science, the Proceedings is the best way to stay informed on an exemplary range of topics.

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Meet Our Editors

Editor-in-Chief
H. Joel Trussell
North Carolina State University