Conditional Random Field for tracking user behavior based on his eye's movements

Trinh Minh Tri Do 1 Thierry Artières 1
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Conditional Random Fields offer some advantages over traditional models for sequence labeling. These conditional models have mainly been introduced up to now in the information retrieval context for information extraction or POS-tagging tasks. This paper investigates the use of these models for signal processing and segmentation. In this context, the input we consider is a signal that is represented as a sequence of real-valued feature vectors and the training is performed using only partially labeled data. We propose a few models for dealing with such signals and provide experimental results on the data from the eye movement challenge.
Type de document :
Communication dans un congrès
NIPS'05 Workshop on Machine Learning for Implicit Feedback and User Modeling, Dec 2005, Whistler, BC, Canada. NIPS'05 Workshop on Machine Learning for Implicit Feedback and User Modeling, pp.19-24
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https://hal.archives-ouvertes.fr/hal-01489443
Contributeur : Lip6 Publications <>
Soumis le : mardi 14 mars 2017 - 14:12:46
Dernière modification le : vendredi 31 août 2018 - 09:25:56

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  • HAL Id : hal-01489443, version 1

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Trinh Minh Tri Do, Thierry Artières. Conditional Random Field for tracking user behavior based on his eye's movements. NIPS'05 Workshop on Machine Learning for Implicit Feedback and User Modeling, Dec 2005, Whistler, BC, Canada. NIPS'05 Workshop on Machine Learning for Implicit Feedback and User Modeling, pp.19-24. 〈hal-01489443〉

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