An unsupervised learning approach for multilayer perceptron networks
Sign Up to like & getrecommendations! Published in 2019 at "Soft Computing"
DOI: 10.1007/s00500-018-3655-2
Abstract: Multilayer perceptron networks have been designed to solve supervised learning problems in which there is a set of known labeled training feature vectors. The resulting model allows us to infer adequate labels for unknown input… read more here.
Keywords: multilayer perceptron; unsupervised learning; model; perceptron networks ... See more keywords
Develop and implement unsupervised learning through hybrid FFPA clustering in large-scale datasets
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DOI: 10.1007/s00500-020-05140-y
Abstract: Clustering is extensively realistic and considered in computer vision that follows unsupervised learning principles. In this, the performance of a clustering process mainly depends on the feature representation. Generally, the clustering process may have an… read more here.
Keywords: clustering process; develop implement; unsupervised learning; ffpa clustering ... See more keywords
Cross-domain person re-identification by hybrid supervised and unsupervised learning
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DOI: 10.1007/s10489-021-02551-8
Abstract: Although the single-domain person re-identification (Re-ID) method has achieved great accuracy, the dependence on the label in the same image domain severely limits the scalability of this method. Therefore, cross-domain Re-ID has received more and… read more here.
Keywords: cross domain; unsupervised learning; domain; supervised unsupervised ... See more keywords
Unsupervised learning to detect loops using deep neural networks for visual SLAM system
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DOI: 10.1007/s10514-015-9516-2
Abstract: This paper is concerned of the loop closure detection problem for visual simultaneous localization and mapping systems. We propose a novel approach based on the stacked denoising auto-encoder (SDA), a multi-layer neural network that autonomously… read more here.
Keywords: visual slam; slam; unsupervised learning; loops using ... See more keywords
Unsupervised learning of finite full covariance multivariate generalized Gaussian mixture models for human activity recognition
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DOI: 10.1007/s11042-018-7116-9
Abstract: We propose in this paper to recognize human activities through an unsupervised learning of finite multivariate generalized Gaussian mixture model. We address an important cue in finite mixture model which is the estimation of the… read more here.
Keywords: multivariate generalized; unsupervised learning; learning finite; mixture ... See more keywords
Unsupervised Learning Based Evaluation of Player Performances
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DOI: 10.1007/s11334-020-00374-3
Abstract: In the following paper, we design a model that uses real-time data to segregate players into categories according to their performances in the T-20 tournaments. The data are gathered from reliable websites, cleaned and analysed… read more here.
Keywords: based evaluation; unsupervised learning; player performances; learning based ... See more keywords
Unsupervised learning on scientific ocean drilling datasets from the South China Sea
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DOI: 10.1007/s11707-018-0704-1
Abstract: Unsupervised learning methods were applied to explore data patterns in multivariate geophysical datasets collected from ocean floor sediment core samples coming from scientific ocean drilling in the South China Sea. Compared to studies on similar… read more here.
Keywords: south china; china sea; unsupervised learning; learning methods ... See more keywords
A New Application of Unsupervised Learning to Nighttime Sea Fog Detection
Sign Up to like & getrecommendations! Published in 2018 at "Asia-Pacific Journal of Atmospheric Sciences"
DOI: 10.1007/s13143-018-0050-y
Abstract: This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the 3.7 μm and 10.8 μm channels of the meteorological imager (MI)… read more here.
Keywords: sea; nighttime sea; unsupervised learning; fog detection ... See more keywords
Unsupervised learning of early post-arrest brain injury phenotypes.
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DOI: 10.1016/j.resuscitation.2020.05.051
Abstract: INTRODUCTION Trials may be neutral when they do not appropriately target the experimental intervention. We speculated multimodality assessment of early hypoxic-ischemic brain injury would identify phenotypes likely to benefit from therapeutic interventions. METHODS We performed… read more here.
Keywords: unsupervised learning; injury phenotypes; arrest; brain ... See more keywords
Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier
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DOI: 10.1016/j.tust.2019.04.019
Abstract: Abstract One of the most serious types of mining disasters in many countries, rockburst leads to injuries, deaths, and damages to facilities, which explains the need to study its prediction. However, due to highly non-linear… read more here.
Keywords: rockburst prediction; learning method; unsupervised learning; prediction ... See more keywords
Unsupervised Learning Methods for Molecular Simulation Data
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DOI: 10.1021/acs.chemrev.0c01195
Abstract: Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide… read more here.
Keywords: unsupervised learning; simulation data; learning methods; methods molecular ... See more keywords