<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhao, Rui</style></author><author><style face="normal" font="default" size="100%">Schalk, Gerwin</style></author><author><style face="normal" font="default" size="100%">Ji, Qiang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust Signal Identification for Dynamic Pattern Classification</style></title><secondary-title><style face="normal" font="default" size="100%">2016 23rd International Conference on Pattern Recognition</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">computational modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">data models</style></keyword><keyword><style  face="normal" font="default" size="100%">Hidden Markov models</style></keyword><keyword><style  face="normal" font="default" size="100%">motion segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">robustness</style></keyword><keyword><style  face="normal" font="default" size="100%">testing</style></keyword><keyword><style  face="normal" font="default" size="100%">Time series analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Dec</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/7900245/</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">3910-3915</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper addresses the problem of identifying signals of interest from discrete-time sequences contaminated by erroneous segments, which we define as the part of time series whose dynamic patterns are inconsistent with that of the signals. Assuming the signals of interest consist of consecutive samples with arbitrary starting point, duration and following a stationary dynamic pattern, we propose a robust algorithm combining Random Sample Consensus (RANSAC) and Hidden Markov Model (HMM) to automatically identify the start and end of signals of interest from time series. To evaluate the identification quality, we perform a classification task, where the identified signals are used to train a classifier. A majority vote strategy is adopted to handle error contaminated testing sequences. Compared with manual selection approach and other unsupervised learning methods, the proposed method shows improvement in classification accuracy on both synthetic and real Electrocorticographic (ECoG) data.</style></abstract></record></records></xml>