A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
Multiple-View Active Learning for Environmental Sound Classification
2016
International Journal of Online Engineering (iJOE)
<p class="Abstract">Multi-view learning with multiple distinct feature sets is a rapid growing direction in machine learning with boosting the performance of supervised learning classification under the case of few labeled data. The paper proposes Multi-view Simple Disagreement Sampling (MV-SDS) and Multi-view Entropy Priority Sampling (MV-EPS) methods as the selecting samples strategies in active learning with multiple-view. For the given environmental sound data, the CELP features in 10
doi:10.3991/ijoe.v12i12.6458
fatcat:yp6zfwf6gna25famcv4ersr3ue