Nov 29, 2019 · Its basic principle is to represent multidimensional paths by a graded feature set of their iterated integrals, called the signature. This ...
Its basic principle is to represent multidimensional paths by a graded feature set of their iterated integrals, called the signature. This approach relies ...
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Its basic principle is to represent multidimensional paths by a graded feature set of their iterated integrals, called the signature. This approach relies ...
Nov 29, 2019 · The present article is concerned with a novel approach for sequential learning, called the signature method, and rooted in rough path theory.
Embedding and learning with signatures · Adeline Fermanian · Published in Computational Statistics… 29 November 2019 · Computer Science, Mathematics.
This approach relies critically on an embedding principle, which consists in representing discretely sampled data as continuous paths. After a survey of basic ...
The present article is concerned with a novel approach for sequential learning, called the signature method and rooted in rough path theory. Its basic principle ...
This approach relies critically on an embedding principle, which consists in representing discretely sampled data as continuous paths. After a survey of basic ...
[PDF] Learning with signatures: embedding and truncation order selection -0.1cm
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Apr 30, 2020 · Learning with signatures: embedding and truncation order selection. DataSig Seminar Series. Adeline Fermanian. April 30th 2020. Page 2. Joint ...
Apr 4, 2022 · We investigate the influence of embeddings on prediction accuracy with an in-depth study of three recent and challenging datasets. We show that ...