Fundamentals of speech recognition
Fundamentals of speech recognition
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Learning visual behavior for gesture analysis
ISCV '95 Proceedings of the International Symposium on Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Integrating Hidden Markov Models and Spectral Analysis for Sensory Time Series Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
International Journal of Robotics Research
Introduction to Information Retrieval
Introduction to Information Retrieval
Online learning with hidden markov models
Neural Computation
Incremental clustering of gesture patterns based on a self organizing incremental neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Classification of temporal data based on self-organizing incremental neural network
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Discovering clusters in motion time-series data
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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In this paper, we introduce machine learning algorithms of time-series data based on Self-organizing Incremental Neural Network (SOINN). SOINN is known as a powerful tool for incremental unsupervised clustering. Using a similarity threshold based and a local error-based insertion criterion, it is able to grow incrementally and to accommodate input patterns of on-line non-stationary data distribution. These advantages of SOINN are available for modeling of time-series data. Firstly, we explain an on-line supervised learning approach, SOINN-DTW, for recognition of time-series data that are based on Dynamic TimeWarping (DTW). Second, we explain an incremental clustering approach, Hidden-Markov-Model Based SOINN (HBSOINN), for time-series data. This paper summarizes SOINN based time-series modeling approaches (SOINN-DTW, HBSOINN) and the advantage of SOINN-based time-series modeling approaches compared to traditional approaches such as HMM.