A Few Useful Things to Know About Machine Learning.
The author discusses machine learning, also known as predictive analytics or data mining, focusing on lessons for computer science researchers and the use of algorithms to perform computational tasks with more efficiency than manual computer programming. The author argues that all machine learning h...
| Published in: | Communications of the ACM Vol. 55; no. 10; pp. 78 - 88 |
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| Format: | Article |
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Association for Computing Machinery
Oct2012
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=82151052&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 82151052 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Oct2012 vid: 55 iid: 10 pid: 68 pub: Association for Computing Machinery artinfo: ui: 82151052 10.1145/2347736.2347755 ppf: 78 ppct: 10 formats: tig: atl: A Few Useful Things to Know About Machine Learning. aug: au: Domingos, Pedro affil: Professor, Department of Computer Science and Engineering, University of Washington, Seattle su: Machine learning Machine theory Computer science Algorithms Analysis of variance Data mining sug: subj: Machine learning Machine theory Computer science Algorithms Analysis of variance Data mining ab: The author discusses machine learning, also known as predictive analytics or data mining, focusing on lessons for computer science researchers and the use of algorithms to perform computational tasks with more efficiency than manual computer programming. The author argues that all machine learning has to do with representation, evaluation, and optimization. Topics include decision trees, probabilistic guarantees, overfitting, and bias and variance in generalization error. Bayesian model averaging (BMA), scalability, and Boolean domains are mentioned. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2012 holdings: @attributes: islocal: N |
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