๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Supervised machine learning techniques for the classification of metabolic disorders in newborns

โœ Scribed by Baumgartner, C.; Bohm, C.; Baumgartner, D.; Marini, G.; Weinberger, K.; Olgemoller, B.; Liebl, B.; Roscher, A. A.


Book ID
115481264
Publisher
Oxford University Press
Year
2004
Tongue
English
Weight
196 KB
Volume
20
Category
Article
ISSN
1367-4803

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

Machine Learning Techniques for the Anal
โœ Khodayari-Rostamabad, A.; Reilly, J.P.; Nikolova, N.K.; Hare, J.R.; Pasha, S. ๐Ÿ“‚ Article ๐Ÿ“… 2009 ๐Ÿ› IEEE ๐ŸŒ English โš– 761 KB

The magnetic flux leakage (MFL) technique, commonly used for nondestructive testing of oil and gas pipelines, involves the detection of defects and anomalies in the pipe wall and the evaluation of the severity of these defects. The difficulty with the MFL method is the extent and complexity of the a