P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00175503" target="_blank" >RIV/68407700:21230/10:00175503 - 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
P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints
Original language description
This paper shows that the performance of a binary clas- sifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if know- ing the label of one example restricts the labeling of the others. We propose anovel paradigm for training a binary classifier from labeled and unlabeled examples that we call P-N learning. The learning process is guided by positive (P) and negative (N) constraints which restrict the label- ing of the unlabeled set. P-N learning evaluates the clas- sifier on the unlabeled data, identifies examples that have been classified in contradiction with structural constraints and augments the training set with the corrected samples in an iterative process. We propose a theory that formu-lates the conditions under which P-N learning guarantees improvement of the initial classifier and validate it on syn- thetic and real data. P-N learning is applied to the problem of on-line learning of object detector during tracking. We
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/GA102%2F07%2F1317" target="_blank" >GA102/07/1317: Methods for Visual Recognition of Large Collections of Non-rigid Objects</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
CVPR 2010: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISBN
978-1-4244-6984-0
ISSN
1063-6919
e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
Omnipress
Place of publication
Madison
Event location
San Francisco
Event date
Jun 13, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000287417500007