Book Chapter10.1007/978-3-319-23192-1_62
Web User Interact Task Recognition Based on Conditional Random Fields
Anis Elbahi,Mohamed Nazih Omri +1 more
- 02 Sep 2015
- pp 740-751
TL;DR: Experimental results show the efficiency of the proposed model and confirm the superiority of Conditional Random Fields approach with respect to the Hidden Markov Models approach in human activity recognition.
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Abstract: Recognition activity of web users based on their navigational behavior during interaction process is an important topic of Human Computer Interaction. To improve the interaction process and interface usability, many studies have been performed for understanding how users interact with a web interface in order to perform a given activity. In this paper we apply the Conditional Random Fields approach for modeling human navigational behavior based on mouse movements to recognize web user tasks. Experimental results show the efficiency of the proposed model and confirm the superiority of Conditional Random Fields approach with respect to the Hidden Markov Models approach in human activity recognition.
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Citations
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Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic Model
TL;DR: The preliminary experiments demonstrate that the sequences of observation obtained based on possibilistic reasoning significantly improve the performance of hidden Marvov models and conditional random fields models in the automatic recognition of the e-learning activities.
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References
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Lawrence R. Rabiner
- 01 Feb 1989
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
•Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
John Lafferty,Andrew McCallum,Fernando Pereira +2 more
- 28 Jun 2001
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
An introduction to hidden Markov models
TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
•Book
Context and consciousness: activity theory and human-computer interaction
Bonnie Nardi
- 02 Dec 1995
TL;DR: In this paper, Nardi proposed activity theory as a potential framework for human-computer interaction research and applied activity theory to video analysis in HCI, and showed that it can be used to make sense of video data.
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