Search Results for author: François Petitjean

Found 6 papers, 6 papers with code

Estimating Divergences in High Dimensions

1 code implementation8 Dec 2021 Loong Kuan Lee, Nico Piatkowski, François Petitjean, Geoffrey I. Webb

To this end, we show empirically that estimating the Kullback-Leibler divergence using decomposable models from a maximum likelihood estimator outperforms existing methods for divergence estimation in situations where dimensionality is high and useful decomposable models can be learnt from the available data.

A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping

1 code implementation25 May 2020 Benjamin Lucas, Charlotte Pelletier, Daniel Schmidt, Geoffrey I. Webb, François Petitjean

In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from SITS data.

Domain Adaptation Time Series

ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels

5 code implementations29 Oct 2019 Angus Dempster, François Petitjean, Geoffrey I. Webb

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.

Classification General Classification +2

Automatic alignment of surgical videos using kinematic data

1 code implementation3 Apr 2019 Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, François Petitjean, Lhassane Idoumghar, Pierre-Alain Muller

Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide.

Dynamic Time Warping Time Series

Understanding Concept Drift

1 code implementation2 Apr 2017 Geoffrey I. Webb, Loong Kuan Lee, François Petitjean, Bart Goethals

Concept drift is a major issue that greatly affects the accuracy and reliability of many real-world applications of machine learning.

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