Feature hashing

Results: 21



#Item
1Feature Hashing for Large Scale Multitask Learning  Kilian Weinberger Anirban Dasgupta John Langford Alex Smola

Feature Hashing for Large Scale Multitask Learning Kilian Weinberger Anirban Dasgupta John Langford Alex Smola

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Source URL: alex.smola.org

Language: English - Date: 2013-09-09 02:03:31
    2POIsketch: Semantic Place Labeling over User Activity Streams 1 Dingqi Yang1 , Bin Li2 , Philippe Cudr´e-Mauroux1 eXascale Infolab, University of Fribourg, 1700 Fribourg, Switzerland 2

    POIsketch: Semantic Place Labeling over User Activity Streams 1 Dingqi Yang1 , Bin Li2 , Philippe Cudr´e-Mauroux1 eXascale Infolab, University of Fribourg, 1700 Fribourg, Switzerland 2

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    Source URL: exascale.info

    Language: English - Date: 2016-08-17 10:09:29
    3BitShred: Feature Hashing Malware for Scalable Triage and Semantic Analysis Jiyong Jang David Brumley

    BitShred: Feature Hashing Malware for Scalable Triage and Semantic Analysis Jiyong Jang David Brumley

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    Source URL: users.ece.cmu.edu

    Language: English - Date: 2014-05-29 15:38:01
    4Resistive Bloom Filters: From Approximate Membership to Approximate Computing with Bounded Errors ∗ Department † Department

    Resistive Bloom Filters: From Approximate Membership to Approximate Computing with Bounded Errors ∗ Department † Department

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    Source URL: mesl.ucsd.edu

    Language: English - Date: 2015-12-02 16:27:13
    5Nordic Collegiate Programming Contest NCPC 2007 October 6th, 2007 The Problemset A B

    Nordic Collegiate Programming Contest NCPC 2007 October 6th, 2007 The Problemset A B

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    Source URL: ncpc.idi.ntnu.no

    Language: English - Date: 2014-01-20 12:10:57
    6CS168: The Modern Algorithmic Toolbox Lecture #2: Approximate Heavy Hitters and the Count-Min Sketch Tim Roughgarden & Gregory Valiant∗ March 30, 2016

    CS168: The Modern Algorithmic Toolbox Lecture #2: Approximate Heavy Hitters and the Count-Min Sketch Tim Roughgarden & Gregory Valiant∗ March 30, 2016

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    Source URL: theory.stanford.edu

    Language: English - Date: 2016-06-04 09:49:43
    7Compressed Representation of Feature Vectors Using a Bloomier Filter and Its Application to Specific Object Recognition Katsufumi Inoue and Koichi Kise Graduate School of Engineering, Osaka Prefecture University 1-1 Gak

    Compressed Representation of Feature Vectors Using a Bloomier Filter and Its Application to Specific Object Recognition Katsufumi Inoue and Koichi Kise Graduate School of Engineering, Osaka Prefecture University 1-1 Gak

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    Source URL: www.m.cs.osakafu-u.ac.jp

    Language: English - Date: 2009-10-05 04:41:49
    8Global Context Descriptors for SURF and MSER Feature Descriptors Gail Carmichael School of Computer Science Carleton University Ottawa, ON, Canada, K1S 5B6

    Global Context Descriptors for SURF and MSER Feature Descriptors Gail Carmichael School of Computer Science Carleton University Ottawa, ON, Canada, K1S 5B6

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    Source URL: www.site.uottawa.ca

    Language: English - Date: 2012-05-22 14:45:00
    9A Precise Analysis of Cuckoo Hashing MICHAEL DRMOTA and REINHARD KUTZELNIGG Technische Universit¨at Wien Cuckoo hashing was introduced by Pagh and Rodler inIts main feature is that it provides constant worst case

    A Precise Analysis of Cuckoo Hashing MICHAEL DRMOTA and REINHARD KUTZELNIGG Technische Universit¨at Wien Cuckoo hashing was introduced by Pagh and Rodler inIts main feature is that it provides constant worst case

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    Source URL: www.dmg.tuwien.ac.at

    Language: English - Date: 2009-03-12 07:46:10
    10BitShred: Feature Hashing Malware for Scalable Triage and Semantic Analysis Jiyong Jang David Brumley

    BitShred: Feature Hashing Malware for Scalable Triage and Semantic Analysis Jiyong Jang David Brumley

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    Source URL: users.ece.cmu.edu

    Language: English - Date: 2014-05-29 15:38:01