Name
Title | ||
|---|---|---|
Classifying the Differences in Gaze Patterns of Alphabetic and Logographic L1 Readers - A Neural Network Approach. |
Abstract | ||
|---|---|---|
Using plain, but large multi-layer perceptrons, temporal eye-tracking gaze patterns of alphabetic and logographic L1 readers were successfully classified. The Eye-tracking data was fed directly into the networks, with no need for pre-processing. Classification rates up to 92% were achieved using MLPs with 4 hidden units. By classifying the gaze patterns of interaction partners, artificial systems are able to act adaptively in a broad variety of application fields. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23957-1_9 | IFIP Advances in Information and Communication Technology |
Field | DocType | Volume |
Gaze,Pattern recognition,Vision span,Reading comprehension,Computer science,Speech recognition,Artificial intelligence,Artificial systems,Artificial neural network,Perceptron | Conference | 363 |
ISSN | Citations | PageRank |
1868-4238 | 1 | 0.36 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| André F. Krause | 1 | 43 | 7.27 |
| KAI ESSIG | 2 | 33 | 4.49 |
| Li-Ying Essig-Shih | 3 | 1 | 0.36 |
| Thomas Schack | 4 | 33 | 7.51 |