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Found 8 papers, 2 papers with code
Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks
Machine learning tasks can be divided into regression and classification.
Data structure > labels? Unsupervised heuristics for SVM hyperparameter estimation
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.
Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation
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.
A Dataset for Evaluating Blood Detection in Hyperspectral Images
To facilitate their development, we present a new hyperspectral blood detection dataset.
Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling
This approach can be applied to many of the hyperspectral classification problems.
General Classification
Hyperspectral Image Classification
+2
Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images
In this paper we present an analysis of a general algorithm for band selection based on higher order cumulants.
Quantum Hidden Markov Models based on Transition Operation Matrices
In this work, we extend the idea of Quantum Markov chains [S. Gudder.
Quantum Physics
Deciding of HMM parameters based on number of critical points for gesture recognition from motion capture data
This paper presents a method of choosing number of states of a HMM based on number of critical points of the motion capture data.
