Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Training Invariant Support Vector Machines
Machine Learning
Learning First Order Logic Time Series Classifiers: Rules and Boosting
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Incorporating Invariances in Support Vector Learning Machines
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Interval and dynamic time warping-based decision trees
Proceedings of the 2004 ACM symposium on Applied computing
Modeling waveform shapes with random effects segmental hidden Markov models
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Exact indexing of dynamic time warping
Knowledge and Information Systems
Embedding of time series data by using dynamic time warping distances
Systems and Computers in Japan
Image deformation using moving least squares
ACM SIGGRAPH 2006 Papers
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning to Transform Time Series with a Few Examples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the VLDB Endowment
Time Series Classification Using Support Vector Machine with Gaussian Elastic Metric Kernel
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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Time-series classification is a field of machine learning that has attracted considerable focus during the recent decades. The large number of time-series application areas ranges from medical diagnosis up to financial econometrics. Support Vector Machines (SVMs) are reported to perform non-optimally in the domain of time series, because they suffer detecting similarities in the lack of abundant training instances. In this study we present a novel time-series transformation method which significantly improves the performance of SVMs. Our novel transformation method is used to enlarge the training set through creating new transformed instances from the support vector instances. The new transformed instances encapsulate the necessary intra-class variations required to redefine the maximum margin decision boundary. The proposed transformation method utilizes the variance distributions from the intra-class warping maps to build transformation fields, which are applied to series instances using the Moving Least Squares algorithm. Extensive experimentations on 35 time series datasets demonstrate the superiority of the proposed method compared to both the Dynamic Time Warping version of the Nearest Neighbor and the SVMs classifiers, outperforming them in the majority of the experiments.