no code implementations • 3 Nov 2021 • Michał Cholewa, Michał Romaszewski, Przemysław Głomb
Classification is one of the main areas of pattern recognition research, and within it, Support Vector Machine (SVM) is one of the most popular methods outside of field of deep learning -- and a de-facto reference for many Machine Learning approaches.
no code implementations • 28 Sep 2021 • Kamil Książek, Przemysław Głomb, Michał Romaszewski, Michał Cholewa, Bartosz Grabowski, Krisztián Búza
Neural networks, in particular autoencoders, are one of the most promising solutions for unmixing hyperspectral data, i. e. reconstructing the spectra of observed substances (endmembers) and their relative mixing fractions (abundances), which is needed for effective hyperspectral analysis and classification.
1 code implementation • 24 Aug 2020 • Michał Romaszewski, Przemysław Głomb, Arkadiusz Sochan, Michał Cholewa
To facilitate their development, we present a new hyperspectral blood detection dataset.
5 code implementations • 10 Aug 2018 • Przemysław Głomb, Krzysztof Domino, Michał Romaszewski, Michał Cholewa
In this paper we present an analysis of a general algorithm for band selection based on higher order cumulants.
no code implementations • 30 Mar 2015 • Michał Cholewa, Piotr Gawron, Przemysław Głomb, Dariusz Kurzyk
In this work, we extend the idea of Quantum Markov chains [S. Gudder.
Quantum Physics
no code implementations • 28 Oct 2011 • Michał Cholewa, Przemysław Głomb
This paper presents a method of choosing number of states of a HMM based on number of critical points of the motion capture data.