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Fine et al., 2002 - Google Patents

Query by committee, linear separation and random walks

Fine et al., 2002

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Document ID
13055075416530787081
Author
Fine S
Gilad-Bachrach R
Shamir E
Publication year
Publication venue
Theoretical Computer Science

External Links

Snippet

A long-standing goal in the realm of Machine Learning is to minimize sample-complexity, ie to reduce as much as possible the number of examples used in the course of learning. The Active Learning paradigm is one such method aimed at achieving this goal by transforming …
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Classifications

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    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
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    • G—PHYSICS
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    • G06Q10/00—Administration; Management

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