Name
Abstract | ||
|---|---|---|
In this paper, systematic three-dimensional classification is presented for sets of interneuron slice images of silkworm moths, using self-organizing maps. Fractal dimension values are calculated for target sets to quantify denseness of their branching structures, and are employed as element values in training data for constructing a map. The other element values are calculated from the sets to which labeling and erosion are applied, and they quantifies whether the sets include thick main dendrites. The classification result is obtained as clusters with units in the map. The proposed classification employing only two elements in training data achieves as high accuracy as the manual classification made by neuroscientists. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74829-8_16 | KES (3) |
Keywords | Field | DocType |
training data,interneuron slice image,fractal dimension value,systematic three-dimensional classification,self-organizing map,manual classification,element value,insect neuron,proposed classification,three-dimensional classification,classification result,high accuracy,three dimensional,fractal dimension,box counting | Training set,Pattern recognition,Fractal dimension,Computer science,Self-organizing map,Artificial intelligence,Box counting,As element,Machine learning,Branching (version control) | Conference |
Volume | ISSN | Citations |
4694 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroki Urata | 1 | 0 | 0.34 |
| Teijiro Isokawa | 2 | 271 | 45.52 |
| Yoich Seki | 3 | 0 | 0.34 |
| Naotake Kamiura | 4 | 179 | 40.05 |
| N. Matsui | 5 | 542 | 89.89 |
| Hidetoshi Ikeno | 6 | 40 | 11.24 |
| Ryohei Kanzaki | 7 | 51 | 18.59 |