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)
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
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
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Machine learning methods have been successfully applied to the phenotype classification of many diseases based on static gene expression measurements. More recently microarray data have been collected over time, making available datasets composed by time series of expression gene profiles. In this paper we propose a new method for time series classification, based on a temporal extension of L1-norm support vector machines, that uses dynamic time warping distance for measuring time series similarity. This results in a mixed-integer optimization model which is solved by a sequential approximation algorithm. Computational tests performed on two benchmark datasets indicate the effectiveness of the proposed method compared to other techniques, and the general usefulness of the approaches based on dynamic time warping for labeling time series gene expression data.