Classification of Spatio-Temporal Trajectories Based on Support Vector Machines
| Autor: | Antonio F. Skarmeta-Gomez, Fernando Terroso-Saenz, Ramon Sanchez-Iborra, Jesus Cuenca-Jara |
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| Rok vydání: | 2018 |
| Předmět: |
Mobility mining
business.industry Computer science Low resolution 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Support vector machine 020204 information systems 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Classifier (UML) computer |
| Zdroj: | Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection ISBN: 9783319945798 PAAMS |
| DOI: | 10.1007/978-3-319-94580-4_11 |
| Popis: | Within the mobility mining discipline, several solutions for the classification of spatio-temporal trajectories have been proposed. However, they usually do not fully consider the particularities of trajectories from human-generated data like online social networks. For that reason, this work introduces a novel classifier based on Support Vector Machines (SVM), which fits the low resolution of this type of geographic data. This solution is applied in a use case for the detection of tourist mobility exhibiting quite promising results. |
| Databáze: | OpenAIRE |
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