๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

A comparison of supervised learning techniques in the classification of bat echolocation calls

โœ Scribed by David W. Armitage; Holly K. Ober


Book ID
119231720
Publisher
Elsevier Science
Year
2010
Tongue
English
Weight
323 KB
Volume
5
Category
Article
ISSN
1574-9541

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Supervised machine learning techniques f
โœ Baumgartner, C.; Bohm, C.; Baumgartner, D.; Marini, G.; Weinberger, K.; Olgemoll ๐Ÿ“‚ Article ๐Ÿ“… 2004 ๐Ÿ› Oxford University Press ๐ŸŒ English โš– 196 KB

## Motivation: During the bavarian newborn screening programme all newborns have been tested for about 20 inherited metabolic disorders. owing to the amount and complexity of the generated experimental data, machine learning techniques provide a promising approach to investigate novel patterns in h

The response of tympanate moths to the e
โœ Paul A. Faure; James H. Fullard; Robert M. R. Barclay ๐Ÿ“‚ Article ๐Ÿ“… 1990 ๐Ÿ› Springer ๐ŸŒ English โš– 685 KB

1. Most studies examining interactions between insectivorous bats and tympanate prey use the echolocation calls of aerially-feeding bats in their analyses. We examined the auditory responses of noctuid (Eurois astricta) and notodontid (Pheosia rimosa) moth to the echolocation call characteristics of