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  • Search: subject:"Kernel technique"
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Year of publication
Subject
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Accelerated gradient 2 Constrained gradient 2 Incremental gradient method 2 Kernel technique 2 Machine learning 2 Nonlinear programming 2 Support vector machines 2 Artificial intelligence 1 Künstliche Intelligenz 1 Market return 1 Mathematical programming 1 Mathematische Optimierung 1 Mustererkennung 1 Nichtlineare Optimierung 1 Optimism (pessimism) 1 Pattern recognition 1 Pricing kernel technique 1 Risk premium 1 Theorie 1 Theory 1 Volatility 1
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Undetermined 2
Type of publication
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Article 3
Type of publication (narrower categories)
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Article in journal 1 Aufsatz in Zeitschrift 1 research-article 1
Language
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English 2 Undetermined 1
Author
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Couellan, Nicolas 2 Jan, Sophie 2 Kapoor, Sujata 1 Prosad, Jaya Mamta 1 Sengupta, Jhumur 1
Published in...
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Computational Management Science 1 Computational Management Science : CMS 1 Review of Behavioral Finance 1
Source
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ECONIS (ZBW) 1 RePEc 1 Other ZBW resources 1
Showing 1 - 3 of 3
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Exploring optimism and pessimism in the Indian equity market
Prosad, Jaya Mamta; Kapoor, Sujata; Sengupta, Jhumur - In: Review of Behavioral Finance 7 (2015) 1, pp. 60-77
optimism (pessimism) using the pricing kernel technique suggested by Barone-Adesi et al. (2012). Second, it tries to analyze …
Persistent link: https://www.econbiz.de/10014990170
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Incremental accelerated gradient methods for SVM classification: study of the constrained approach
Couellan, Nicolas; Jan, Sophie - In: Computational Management Science 11 (2014) 4, pp. 419-444
We investigate constrained first order techniques for training support vector machines (SVM) for online classification tasks. The methods exploit the structure of the SVM training problem and combine ideas of incremental gradient technique, gradient acceleration and successive simple...
Persistent link: https://www.econbiz.de/10010949671
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Cover Image
Incremental accelerated gradient methods for SVM classification : study of the constrained approach
Couellan, Nicolas; Jan, Sophie - In: Computational Management Science : CMS 11 (2014) 4, pp. 419-444
Persistent link: https://www.econbiz.de/10010437148
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