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We present a novel framework for kernel learning with sequential data of any kind, such as time series, sequences of graphs, or strings.
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Differential equations driven by rough paths
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Kernel methods for pattern analysis
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A kernel for time series based on global alignments
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Rotation invariants of two dimensional curves based on iterated integrals
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Sparse arrays of signatures for online character recognition
Benjamin Graham · 2013
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Extracting information from the signature of a financial data stream
Lajos Gergely Gyurkó, Terry Lyons, Mark Kontkowski, and Jonathan Field · 2013
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Learning from the past, predicting the statistics for the future, learning an evolving system
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A generalization of haussler’s convolution kernel: mapping kernel
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Multidimensional stochastic processes as rough paths: theory and applications
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Uniqueness for the signature of a path of bounded variation and the reduced path group
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Fast global alignment kernels
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Parameter estimation for rough differential equations
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Emg based classification of basic hand movements based on time-frequency features
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The Signature of a Rough Path: Uniqueness
H. Boedihardjo, X. Geng, T. Lyons, and D. Yang · 2014
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A theory of regularity structures
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A feature set for streams and an application to high-frequency financial tick data
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Statistics for spatial data
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Character-level chinese writer identification using path signature feature, dropstroke and deep CNN
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Computational Rough Paths Project on SourceForge
Djèlil Chafaï, Terry Lyons, Christoph Ladroue, and Anastasia Papavasiliou · 2016
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