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Supervised classification of human microbiota [PDF]
FEMS Microbiology Reviews, 2011 Recent advances in DNA sequencing technology have allowed the collection of high-dimensional data from human-associated microbial communities on an unprecedented scale. A major goal of these studies is the identification of important groups of microorganisms that vary according to physiological or disease states in the host, but the incidence of rare ...
Dan, Knights +2 more
openaire +2 more sources
Semi-Supervised DEGAN for Optical High-Resolution Remote Sensing Image Scene Classification
Remote Sensing, 2022 Semi-supervised methods have made remarkable achievements via utilizing unlabeled samples for optical high-resolution remote sensing scene classification.
Jia Li +4 more
doaj +1 more source
Genetic Classification of Populations using Supervised Learning [PDF]
, 2010 There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure.
A Motsinger-Reif +26 more
core +9 more sources
Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours
AAAI Conference on Artificial Intelligence, 2017 Due to the efficiency of learning relationships and complex structures hidden in data, graph-oriented methods have been widely investigated and achieve promising performance in multi-view learning.
F. Nie, Guohao Cai, Xuelong Li
semanticscholar +1 more source
Remote Sensing, 2022 In recent years, supervised learning, represented by deep learning, has shown good performance in remote sensing image scene classification with its powerful feature learning ability. However, this method requires large-scale and high-quality handcrafted
Xiliang Chen, Guobin Zhu, Mingqing Liu
doaj +1 more source
Weakly-Supervised Neural Text Classification
, 2018 Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from
Han, Jiawei +3 more
core +1 more source
Remote Sensing, 2020 Many automatic landslide detection algorithms are based on supervised classification of various remote sensing (RS) data, particularly satellite images and digital elevation models (DEMs) delivered by Light Detection and Ranging (LiDAR). Machine learning
Kamila Pawluszek-Filipiak, A. Borkowski
semanticscholar +1 more source
Fine-Grained Classification of Hyperspectral Imagery Based on Deep Learning
Remote Sensing, 2019 Hyperspectral remote sensing obtains abundant spectral and spatial information of the observed object simultaneously. It is an opportunity to classify hyperspectral imagery (HSI) with a fine-grained manner.
Yushi Chen +4 more
doaj +1 more source
Road Detection through Supervised Classification
, 2016 Autonomous driving is a rapidly evolving technology. Autonomous vehicles are capable of sensing their environment and navigating without human input through sensory information such as radar, lidar, GNSS, vehicle odometry, and computer vision.
Alkhorshid, Yasamin +3 more
core +1 more source
Automated supervised classification of variable stars I. Methodology [PDF]
, 2007 The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first.
Aerts, C. +6 more
core +4 more sources