http://bactra.org/notebooks Cosma's Notebooks en http://bactra.org/notebooks/2011/10/28#kernel-classifiers-for-time-series <P>Yet Another Placeholder. <ul>To read: <li>Marco Cuturi, Arnaud Doucet, "Autoregressive Kernels For Time Series", <a href="http://arxiv.org/abs/1101.0673">arxiv:1101.0673</a> [In which two time series are similar if they are fit by similar autoregressive models] <li>Marco Cuturi, Jean-Philippe Vert, Oystein Birkenes, and Tomoko Matsui, "A kernel for time series based on global alignments", <a href="http://arxiv.org/abs/cs.CV/0610033">cs.CV/0610033</a> <li>Scott Lenser and Manuela Veloso, "Non-Parametric Time Series Classification" [<a href="http://penguin.coral.cs.cmu.edu/slenser/scott_lenser_resume/papers/papers/thesis/prediction_icra05_under_review.pdf">PDF preprint</a>] <li>T. Warren Liao, "Clustering of time series data---a survey", <a href="http://dx.doi.org/10.1016/j.patcog.2005.01.025"><cite>Pattern Recognition</cite> <strong>38</strong> (2005): 1857--1874</a> <li>Zhengdong Lu, Todd K. Leen and Jeffrey Kaye, "Kernels for Longitudinal Data with Variable Sequence Length and Sampling Intervals", <a href="http://dx.doi.org/10.1162/NECO_a_00164"><cite>Neural Computation</cite> <strong>23</strong> (2011): 2390--2420</a> <li>Nishant A. Mehta and Alexander G. Gray, "Generative and Latent Mean Map Kernels", <a href="http://arxiv.org/abs/1005.0188">arxiv:1005.0188</a> <li>Vitaly Schetinin and Joachim Schult, "Learning Polynomial Networks for Classification of Clinical Electroencephalograms", <a href="http://arxiv.org/abs/cs.AI/0504041">cs.AI/0504041</a> [Not a kernel method, at least not to judge by the abstract, but very close to the application domain we have in mind] <li>Ansgar Steland, "Optimal sequential kernel detection for dependent processes", <a href="http://dx.doi.org/10.1016/j.jspi.2004.06.019"><cite>Journal of Statistical Planning and Inference</cite> <strong>132</strong> (2005): 131--147</a> </ul>