Unsupervised Machine Learning for Unbiased Chemical Classification in X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy [PDF]
, 2021 We report a comprehensive computational study of unsupervised machine learning for extraction of chemically relevant information in X-ray absorption near edge structure (XANES) and in valence-to-core X-ray emission spectra (VtC-XES) for classification of
Samantha, Tetef +2 more
core +2 more sources
CBARS: cluster based classification for activity recognition systems [PDF]
, 2012 Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition.
Gaber, Mohamed Medhat +11 more
core +1 more source
Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning [PDF]
, 2014 Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies.
Plumbley, Mark D. +6 more
core +2 more sources
ACCURACY OF UNSUPERVISED CLASSIFICATION TO DETERMINE CORAL HEALTH USING SPOT-6 AND SENTINEL-2A [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Characteristics of corals spectral from different species are expected to have optically different characters. The aims of this research are to compare unsupervised classification between IsoData and K-Means methods with Lyzenga application, and to ...
N. Nurdin +7 more
doaj +1 more source
Unsupervised classification of variable stars [PDF]
Monthly Notices of the Royal Astronomical Society, 2017 Accepted in November ...
Valenzuela, Lucas +1 more
openaire +3 more sources
Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study [PDF]
, 2010 In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors.
Laura M. McGarry +11 more
core +1 more source
Unsupervised Spatial-Spectral CNN-Based Feature Learning for Hyperspectral Image Classification
, 2022 The rapid development of remote sensing sensors makes the acquisition, analysis, and application of hyperspectral images (HSIs) more and more extensive.
Jia, Sen +3 more
core +1 more source
Remote SensingSupervised polarimetric synthetic aperture radar (PolSAR) image classification demands a large amount of precisely labeled data. However, such data are difficult to obtain.
Lei Wang +4 more
doaj +1 more source
Multi-sensor remote sensing information fusion for urban area classification and change detection [PDF]
, 2011 Information extraction from multi-sensor remote sensing imagery is an important and challenging task for many applications such as urban area mapping and change detection. A special acquisition (orthogonal) geometry is of great importance for optical and
Palubinskas, Gintautas +5 more
core +1 more source
Collocation Classification with Unsupervised Relation Vectors [PDF]
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019 Lexical relation classification is the task of predicting whether a certain relation holds between a given pair of words. In this paper, we explore to which extent the current distributional landscape based on word embeddings provides a suitable basis for classification of collocations, i.e., pairs of words between which idiosyncratic lexical relations
Luis Espinosa Anke +2 more
openaire +3 more sources