{RDF::Dataset} is introduced as a class alias of {RDF::Repository}. This allows closer alignment to the RDF concept ofDataset. Thegraph_nameof a graph within a Dataset or Repository may be either an {RDF::IRI} or {RDF::Node}. Implementations of repositories may restrict this to being ...
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We consider well-known datasets (such as iris and zoo) to illustrate our approach. Furthermore, we extend this analysis to intricate datasets, notably the GTZAN musical dataset and the "Adult" dataset. These examples showcase the algorithm's efficacy in generating descriptive rules across ...
This chapter develops a generalVision Metrics Taxonomyfor feature description, so as to collect summary descriptor attributes for high-level analysis. The taxonomy includes a set of generalrobustness criteriafor feature description and ground truth datasets. The material presented and discussed in this bo...
|[Dumb-Dataset-Linear-Regression](https://github.com/avinash-218/ML-Playground/tree/master/Supervised%20Learning/Dumb-Dataset-Linear-Regression)|| |[Emotion-Recognition-HaarCascade](https://github.com/avinash-218/ML-Playground/tree/master/Supervised%20Learning/Emotion-Recognition-HaarCascade)|| |[Encod...
Taking the Iris dataset as an example, when the proportion of noise-contaminated data r increases from 0.1 to 0.5, the G-means of ϕ R -SVDD, ϕ f -SVDD, and ϕ L -SVDD decline by 13.02%, 13.02%, and 11.5%, while those of the DW-SVDD, R-SVDD, and GLE-SVDD decline by ...
An instance 𝑀(𝐺)M(G) of an RDF graph G maps IRIs and literals to themselves, while a blank node is mapped to a term (another blank node, or IRI, or literal). Thanks to its higher-order semantics (which we do not discuss here), RDF is equipped with a simple entailment relatio...
Users start by selecting the data product to display (i.e., the satellite mission and desired dataset) and optionally a date interval filter. Then there is the Data Granules Management system that allows users to set the allocation of the amount of data granules that are processed each time....
It was found that RF and DNN performed better on the global dataset, while k-NN performed better on the unbalanced acute toxicity datasets. This result also highlights the importance of neighbor information in acute toxicity prediction [126]. In order to adapt the chemical descriptor to the ...