Hidden Markov Models Used for the Offline Classification of EEG Data - Hidden Markov-Modelle, verwendet zur Offline-Klassifikation von EEG-Daten

1999 ◽  
Vol 44 (6) ◽  
pp. 158-162 ◽  
Author(s):  
B. Obermaier ◽  
Ch. Guger ◽  
G. Pfurtscheller
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Eeg Data

Hidden Markov models for online classification of single trial EEG data

2001 ◽  
Vol 22 (12) ◽  
pp. 1299-1309 ◽  
Author(s):  
B Obermaier ◽  
C Guger ◽  
C Neuper ◽  
G Pfurtscheller
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Single Trial ◽  
Online Classification ◽  
Eeg Data

scholarly journals CutProtFam-Pred: Detection and classification of putative structural cuticular proteins from sequence alone, based on profile Hidden Markov Models

2014 ◽  
Vol 52 ◽  
pp. 51-59 ◽  
Author(s):  
Zoi S. Ioannidou ◽  
Margarita C. Theodoropoulou ◽  
Nikos C. Papandreou ◽  
Judith H. Willis ◽  
Stavros J. Hamodrakas
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Profile Hidden Markov Models ◽  
Cuticular Proteins

scholarly journals Sequence Classification Using Third-Order Moments

2018 ◽  
Vol 30 (1) ◽  
pp. 216-236
Author(s):  
Rasmus Troelsgaard ◽  
Lars Kai Hansen
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Sequence Data ◽  
Hidden Markov ◽  
Score Function ◽  
Simulated Data ◽  
Discrete Observations ◽  
Third Order ◽  
Leibler Divergence

Model-based classification of sequence data using a set of hidden Markov models is a well-known technique. The involved score function, which is often based on the class-conditional likelihood, can, however, be computationally demanding, especially for long data sequences. Inspired by recent theoretical advances in spectral learning of hidden Markov models, we propose a score function based on third-order moments. In particular, we propose to use the Kullback-Leibler divergence between theoretical and empirical third-order moments for classification of sequence data with discrete observations. The proposed method provides lower computational complexity at classification time than the usual likelihood-based methods. In order to demonstrate the properties of the proposed method, we perform classification of both simulated data and empirical data from a human activity recognition study.

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