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Online trajectory classification

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Online trajectory classification. / Sas, Corina; O'Hare, Gregory; Reilly, Ronan.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2659, 01.12.2003, p. 1035-1044.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Sas, C, O'Hare, G & Reilly, R 2003, 'Online trajectory classification', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2659, pp. 1035-1044. https://doi.org/10.1007/3-540-44863-2_102

APA

Sas, C., O'Hare, G., & Reilly, R. (2003). Online trajectory classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2659, 1035-1044. https://doi.org/10.1007/3-540-44863-2_102

Vancouver

Sas C, O'Hare G, Reilly R. Online trajectory classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2003 Dec 1;2659:1035-1044. doi: 10.1007/3-540-44863-2_102

Author

Sas, Corina ; O'Hare, Gregory ; Reilly, Ronan. / Online trajectory classification. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2003 ; Vol. 2659. pp. 1035-1044.

Bibtex

@article{31431523943548d899d8e37e2eb6557e,
title = "Online trajectory classification",
abstract = "This study proposes a modular system for clustering on-line motion trajectories obtained while users navigate within a virtual environment. It presents a neural network simulation that gives a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organizing map algorithm was tested and improved to above 85% by using learning vector quantization. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction are discussed.",
author = "Corina Sas and Gregory O'Hare and Ronan Reilly",
year = "2003",
month = dec,
day = "1",
doi = "10.1007/3-540-44863-2_102",
language = "English",
volume = "2659",
pages = "1035--1044",
journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
issn = "0302-9743",
publisher = "Springer Verlag",

}

RIS

TY - JOUR

T1 - Online trajectory classification

AU - Sas, Corina

AU - O'Hare, Gregory

AU - Reilly, Ronan

PY - 2003/12/1

Y1 - 2003/12/1

N2 - This study proposes a modular system for clustering on-line motion trajectories obtained while users navigate within a virtual environment. It presents a neural network simulation that gives a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organizing map algorithm was tested and improved to above 85% by using learning vector quantization. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction are discussed.

AB - This study proposes a modular system for clustering on-line motion trajectories obtained while users navigate within a virtual environment. It presents a neural network simulation that gives a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organizing map algorithm was tested and improved to above 85% by using learning vector quantization. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction are discussed.

U2 - 10.1007/3-540-44863-2_102

DO - 10.1007/3-540-44863-2_102

M3 - Journal article

AN - SCOPUS:27544480646

VL - 2659

SP - 1035

EP - 1044

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SN - 0302-9743

ER -