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The purpose of this research was to develop a knowledge recommendation architecture based on unsupervised machine learning and unified resource description framework (RDF) for integrated environmental sensory data sources.
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In this study, nonlinear principal component analysis based on unsupervised support vector machine is introduced and incorporated with a discrete-time prediction model and a hypothesis test for data normalization.
In short, the main contributions of this paper are fourfold: (i) to employ unsupervised OPF for non-technical losses detection, (ii) to model the problem of NTL as being an anomaly detection task, (iii) to employ unsupervised OPF to estimate the parameters of the Gaussian distributions, and (iv) to present an anomaly detection approach based on unsupervised optimum-path forest.
This paper addresses the problem of urban scene monitoring and tracking of obstacles based on unsupervised, deep-learning approaches.
To this end, feedforward neural networks (FNNs) and a feature selection method based on unsupervised clustering, were employed.
According to the context and available data, it is based on unsupervised, supervised or semi-supervised algorithms [34].
Analysis based on unsupervised factor models was carried out with publicly available software as previously described [18].
Patients stratification based on unsupervised hierarchical clustering divided patients into two groups.
SOM, an artificial intelligence algorithm based on unsupervised learning configures output vectors into a topological presentation of the original data.
Moreover, these results based on unsupervised analysis yielded a signature predictive of clinical outcome in breast cancer.
We wish to explore an alternative analysis approach, based on unsupervised, nonlinear, topographic (structure-preserving) projection and visualisation methods.
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