Pneumonia and asthma are among the top causes of death worldwide with 300 million people suffered. In the year 2005, 255,000 people died only because of asthma . Good controlling requires both proper medication and continual monitoring over days and nights. In this paper, we introduce a novel classifier, namely Semi- Supervised Tree Support Vector Machine, to target the problem of cough detection and monitoring. It will adaptively analyze the distribution of samples' confidence metrics, automatically select the most informative samples and re-train the core Tree SVM classifier inside accordingly. Besides, we also introduce a new way to build Tree SVM, based on Fisher Linear Discriminant (FLD) analytic. All are meant to improve final system performance, and our proposed classifier has really demonstrated good improvement over conventional method; validated on a database consists of comprehensive body-sounds, recorded with wearable contact microphone.
Bibliographic reference. Huynh, Thai Hoa / Tran, Vu An / Tran, Huy Dat (2011): "Semi-supervised tree support vector machine for online cough recognition", In INTERSPEECH-2011, 1637-1640.