Fundamentals of speech recognition
Fundamentals of speech recognition
Practical genetic algorithms
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Distance Measures for Effective Clustering of ARIMA Time-Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The Journal of Machine Learning Research
Probabilistic Kernels for the Classification of Auto-Regressive Visual Processes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Discriminatively Trained Markov Model for Sequence Classification
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Large margin training of acoustic models for speech recognition
Large margin training of acoustic models for speech recognition
Engineering Applications of Artificial Intelligence
Large margin hidden Markov models for speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
The influence of global constraints on similarity measures for time-series databases
Knowledge-Based Systems
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In this paper, we propose the large margin autoregressive (LMAR) model for classification of time series patterns. The parameters of the generative AR models for different classes are estimated using the margin of the boundaries of AR models as the optimization criterion. Models that use a mixture of AR (MAR) models are considered for representing the data that cannot be adequately represented using a single AR model for a class. Based on a mixture model representing each class, we propose the large margin mixture of AR (LMMAR) models. The proposed methods are applied on the simulated time series data, electrocardiogram data, speech data for E-set in English alphabet and electroencephalogram time series data. Performance of the proposed methods is compared with that of support vector machine (SVM) based classifier that uses AR coefficients based features. The proposed methods give a better classification performance compared to the SVM based classifier. Being generative models, the LMAR and LMMAR models provide a generative interpretation that enables utilization of the rejection option in the high risk classification tasks. The proposed methods can also be used for detection of novel time series data.