@InProceedings{Weilhammer2016_325,
<br/>   author = {Karl Weilhammer and Prince Kumar and Volker Springer and Dominique Massonie},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2016},
<br/>   title = {Spoken Language Understanding in Embedded Systems},
<br/>   year = {2016},
<br/>   editor = {Oliver Jokisch},
<br/>   month = mar,
<br/>   pages = {69--76},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {We investigated classification with Support Vector Machines for spoken
language understanding with respect to their use in embedded devices, which are
often equipped with slow CPUs, and main or persistent memory of limited size or
with slow access times. We started with uni- and bigrams as features and managed to
reduce the feature set in most cases by applying Recursive Feature Elimination from
a few thousands to a few dozens. This corresponds to a reduction of the overall
model size to 6% of the original size, without having a substantial loss in
classification performance. The F score difference is 0.16%.},
<br/>   isbn = {978-3-959080-40-8},
<br/>   issn = {0940-6832},
<br/>   keywords = {Spracherkennung und Dialogsysteme},
<br/>   url = {https://www.essv.de/pdf/2016_69_76.pdf},
<br/>}