error-correcting output hashing in fast similarity search Benet Lake Wisconsin

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error-correcting output hashing in fast similarity search Benet Lake, Wisconsin

OK WebImagesMore…Sign inExport articlesExport selected articlesExport all my articlesExportCancelMerged citationsThis "Cited by" count includes citations to the following articles in Scholar. It’s like Spotify but for academic articles.” @Phil_Robichaud “I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.” @deepthiw “My last article couldn't be US & Canada: +1 800 678 4333 Worldwide: +1 732 981 0060 Contact & Support About IEEE Xplore Contact Us Help Terms of Use Nondiscrimination Policy Sitemap Privacy & Opting Out In [35], a binary ECOC matrix is constructed in which a codeword is considered as a target hash code for a particular label.

Monthly Plan Read unlimited articles Personalized recommendations Print 20 pages per month 20% off on PDF purchases Organize your research Get updates on your journals and topic searches $40/month Best Deal If the frequency of update is high, then recomputing the hash table entries may cause inefficiencies in the system, especially for large indexes. Recently, hashing-based methods, which create compact and e ƒcient codes that preserve data distribution, have received considerable attention due to their promising theoretical and empirical results. morefromWikipedia Elliptic curve only hash The elliptic curve only hash (ECOH) algorithm was submitted as a candidate for SHA-3 in the NIST hash function competition.

Enjoy unlimited access and personalized recommendations from over 12 million articles from more than 10,000 peer-reviewed journals. Read our cookies policy to learn more.OkorDiscover by subject areaRecruit researchersJoin for freeLog in EmailPasswordForgot password?Keep me logged inor log in with An error occurred while rendering template. MargaritisRead full-textClustering Approaches for Financial Data Analysis: a Survey Full-text · Article · Sep 2016 Fan CaiNhien-An Le-KhacTahar KechadiRead full-textData provided are for informational purposes only. The values returned by a hash function are called hash values, hash codes, hash sums, checksums or simply hashes.

Log in Sign up - Try 2 Weeks Free DeepDyve How it Works Content Pricing Search Browse Subject Areas Journals Publishers Filters Title Journal Authors Date Anytime Within the last Your cache administrator is webmaster. Their combined citations are counted only for the first article.DoneMerge duplicatesCitations per yearScholarFollowEmailFollow new articlesFollow new citationsCreate alertCancelZhou YuCarnegie Mellon UniversityArtificial Intelligence, Dialog System, Multimodal Analysis, Natural Language ProcessingVerified email at Use of this web site signifies your agreement to the terms and conditions.

The problem is: given a set S of points in a metric space M and a query point q ¿ M, find the closest point in S to q. Did you know your Organization can subscribe to the ACM Digital Library? Please try the request again. Please try again!

For example, a person's name, having a variable length, could be hashed to a single integer. Unfortunately, batch-learning strategies can be inefficient when confronted with large training datasets. Your cache administrator is webmaster. Institutional Sign In By Topic Aerospace Bioengineering Communication, Networking & Broadcasting Components, Circuits, Devices & Systems Computing & Processing Engineered Materials, Dielectrics & Plasmas Engineering Profession Fields, Waves & Electromagnetics General

morefromWikipedia Linear regression In statistics, linear regression is an approach to modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X. To effectively deal with the error accumulated during converting the real-value embeddings into the binary code after relaxation, we propose a semi-supervised nonlinear hashing algorithm using bootstrap sequential projection learning which Please try the request again. Full-text · Article · Mar 2016 · IEEE Transactions on Knowledge and Data EngineeringJoey Tianyi ZhouIvor W.

The ECOH is based on the MuHASH hash algorithm, that has not yet been successfully attacked. All rights reserved.About us · Contact us · Careers · Developers · News · Help Center · Privacy · Terms · Copyright | Advertising · Recruiting We use cookies to give you the best possible experience on ResearchGate. The ones marked * may be different from the article in the profile.DoneDuplicate citationsThe following articles are merged in Scholar. Subjects: Information Retrieval (cs.IR) Citeas: arXiv:1004.5370 [cs.IR] (or arXiv:1004.5370v1 [cs.IR] for this version) Submission history From: Dell Zhang [view email] [v1] Thu, 29 Apr 2010 19:25:17 GMT (95kb) Which authors

Stay up to date Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates. Your journals are on DeepDyve Read from thousands of the leading scholarly journals from Springer, Elsevier, Nature, IEEE, Wiley-Blackwell and more. Although carefully collected, accuracy cannot be guaranteed. Please enable Javascript on your browser to continue. “Whoa!

An ideal hashing method 1) can naturally have out-of-sample extension; 2) has very low computational complexity; and 3) has signi cant improvement over linear search in the original space in terms Thus, we also propose a framework to reduce hash table updates. In this paper, we emphasise this issue and propose a novel Self-Taught Hashing (STH) approach to semantic hashing: we first find the optimal $l$-bit binary codes for all documents in the The ACM Guide to Computing Literature All Tags Export Formats Save to Binder For full functionality of ResearchGate it is necessary to enable JavaScript.

See all ›6 CitationsSee all ›20 ReferencesShare Facebook Twitter Google+ LinkedIn Reddit Request full-text Error-Correcting Output Hashing in fast similarity searchArticle · January 2010 with 6 ReadsDOI: 10.1145/1937728.1937730 1st Zhou Yu2nd Deng Cai3rd Xiaofei HeAbstractFast Get Help About IEEE Xplore Feedback Technical Support Resources and Help Terms of Use What Can I Access? morefromWikipedia Hash function A hash function is any algorithm or subroutine that maps large data sets of variable length, called keys, to smaller data sets of a fixed length. Another category is the hashing-based method [3], [12], [5], [13], [9], [23], [34], [28], [16], [10], [37], [31], [32], [19], [35], [36], which has recently attracted considerable attention to achieve the

Our experiments on three real-world text datasets show that the proposed approach using binarised Laplacian Eigenmap (LapEig) and linear Support Vector Machine (SVM) outperforms state-of-the-art techniques significantly. More than one explanatory variable is multiple regression. (This in turn should be distinguished from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable. DeepDyve is your personal research library It’s your single place to instantly discover and read the research that matters to you. Terms of Service | Privacy Policy | Blog × Sign up for your 14-Day Free Trial now Read and print from thousands of top scholarly journals.

We compare our method to state-of-the-art solutions on two benchmarks and demonstrate significant improvements over previous work. In this paper, we focus on the learning-based hashing methods. "[Show abstract] [Hide abstract] ABSTRACT: In this paper, we study the effective semi-supervised hashing method under the framework of regularized learning-based rgreq-ca66b90d3583f0ddc09a325807c4ac65 false Cornell University Library We gratefully acknowledge support fromthe Simons Foundation and The Alliance of Science Organisations in Germany, coordinated by TIB, MPG and HGF > cs > arXiv:1004.5370 It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.

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