The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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)
Algorithms in Java, Part 5: Graph Algorithms
Algorithms in Java, Part 5: Graph Algorithms
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Interval and dynamic time warping-based decision trees
Proceedings of the 2004 ACM symposium on Applied computing
Distance-function design and fusion for sequence data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Exact indexing of dynamic time warping
Knowledge and Information Systems
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Indexing Multidimensional Time-Series
The VLDB Journal — The International Journal on Very Large Data Bases
Accurately learning from few examples with a polyhedral classifier
Computational Optimization and Applications
Cybernetics and Systems Analysis
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Time series gene expression data classification via L1-norm temporal SVM
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Artificial Intelligence in Medicine
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Time series classification is a supervised learning problem aimed at labeling temporally structured multivariate sequences of variable length. The most common approach reduces time series classification to a static problem by suitably transforming the set of multivariate input sequences into a rectangular table composed by a fixed number of columns. Then, one of the alternative efficient methods for classification is applied for predicting the class of new temporal sequences. In this paper, we propose a new classification method, based on a temporal extension of discrete support vector machines, that benefits from the notions of warping distance and softened variable margin. Furthermore, in order to transform a temporal dataset into a rectangular shape, we also develop a new method based on fixed cardinality warping distances. Computational tests performed on both benchmark and real marketing temporal datasets indicate the effectiveness of the proposed method in comparison to other techniques.