Zobrazeno 1 - 10
of 235
pro vyhledávání: '"PETITJEAN, FRANÇOIS"'
Autor:
Ismail-Fawaz, Ali, Fawaz, Hassan Ismail, Petitjean, François, Devanne, Maxime, Weber, Jonathan, Berretti, Stefano, Webb, Geoffrey I., Forestier, Germain
Time series data can be found in almost every domain, ranging from the medical field to manufacturing and wireless communication. Generating realistic and useful exemplars and prototypes is a fundamental data analysis task. In this paper, we investig
Externí odkaz:
http://arxiv.org/abs/2309.16353
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 37, 10 (Jun. 2023), 12243-12251
There are many applications that benefit from computing the exact divergence between 2 discrete probability measures, including machine learning. Unfortunately, in the absence of any assumptions on the structure or independencies within these distrib
Externí odkaz:
http://arxiv.org/abs/2112.04583
This paper contributes multivariate versions of seven commonly used elastic similarity and distance measures for time series data analytics. Elastic similarity and distance measures are a class of similarity measures that can compensate for misalignm
Externí odkaz:
http://arxiv.org/abs/2102.10231
Autor:
Webb, Geoffrey I., Petitjean, Francois
Publikováno v:
Pattern Recognition, Volume 115, 2021, 107895, ISSN 0031-3203
Dynamic Time Warping (DTW) is a popular similarity measure for aligning and comparing time series. Due to DTW's high computation time, lower bounds are often employed to screen poor matches. Many alternative lower bounds have been proposed, providing
Externí odkaz:
http://arxiv.org/abs/2102.07076
Supervised learning, characterized by both discriminative and generative learning, seeks to predict the values of single (or sometimes multiple) predefined target attributes based on a predefined set of predictor attributes. For applications where th
Externí odkaz:
http://arxiv.org/abs/2011.06428
Publikováno v:
International Joint Conference on Neural Networks (IJCNN 2020)
Stream classification methods classify a continuous stream of data as new labelled samples arrive. They often also have to deal with concept drift. This paper focuses on seasonal drift in stream classification, which can be found in many real-world a
Externí odkaz:
http://arxiv.org/abs/2006.15311
This paper studies Time Series Extrinsic Regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims
Externí odkaz:
http://arxiv.org/abs/2006.12672
Time series research has gathered lots of interests in the last decade, especially for Time Series Classification (TSC) and Time Series Forecasting (TSF). Research in TSC has greatly benefited from the University of California Riverside and Universit
Externí odkaz:
http://arxiv.org/abs/2006.10996
Autor:
Lucas, Benjamin, Pelletier, Charlotte, Schmidt, Daniel, Webb, Geoffrey I., Petitjean, François
Land cover maps are a vital input variable to many types of environmental research and management. While they can be produced automatically by machine learning techniques, these techniques require substantial training data to achieve high levels of a
Externí odkaz:
http://arxiv.org/abs/2005.11930
Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets. Additionally, many existing
Externí odkaz:
http://arxiv.org/abs/1910.13051