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Original title:
Learning with Generalization Capability by Kernel Methods of Bounded Complexity
Authors: Kůrková, Věra ; Sanguineti, M.
Document type: Research reports
Year: 2003
Language: eng
Series: Technical Report, volume: V-901
Keywords: generalization; kernel methods; minimization of regularized empirical errors; model complexity; supervised learning; upper bounds on rates of approximate optimization
Project no.: AV0Z1030915 (CEP), GA201/02/0428 (CEP), Project 22
Funding provider: GA ČR, IT-CZ Area MC6
Rights: This work is protected under the Copyright Act No. 121/2000 Coll.
Institution: Institute of Computer Science AS ČR (web)
Original record: http://hdl.handle.net/11104/0125385
Permalink: http://www.nusl.cz/ntk/nusl-34137
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Reports > Research reports
Authors: Kůrková, Věra ; Sanguineti, M.
Document type: Research reports
Year: 2003
Language: eng
Series: Technical Report, volume: V-901
Keywords: generalization; kernel methods; minimization of regularized empirical errors; model complexity; supervised learning; upper bounds on rates of approximate optimization
Project no.: AV0Z1030915 (CEP), GA201/02/0428 (CEP), Project 22
Funding provider: GA ČR, IT-CZ Area MC6
Rights: This work is protected under the Copyright Act No. 121/2000 Coll.
Institution: Institute of Computer Science AS ČR (web)
Original record: http://hdl.handle.net/11104/0125385
Permalink: http://www.nusl.cz/ntk/nusl-34137
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Reports > Research reports
Record created 2011-07-01, last modified 2024-01-26
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