Support Vector Machines as Probabilistic Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00187113" target="_blank" >RIV/68407700:21230/11:00187113 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Support Vector Machines as Probabilistic Models
Original language description
We show how the SVM can be viewed as a maximum likelihood estimate of a class of probabilistic models. This model class can be viewed as a reparametrization of the SVM in a similar vein to the v-SVM reparametrizing the classical (C-)SVM. It is not discriminative, but has a non-uniform marginal. We illustrate the benefits of this new view by re-deriving and re-investigating two established SVM-related algorithms.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/1M0567" target="_blank" >1M0567: Centre for Applied Cybernetics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 28th Annual International Conference on Machine Learning (ICML 2011)
ISBN
978-1-4503-0619-5
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
665-672
Publisher name
ACM
Place of publication
New York
Event location
Bellevue
Event date
Jun 28, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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