Data from: A demonstration of unsupervised machine learning in species delimitation

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Paranonychidae;integrative taxonomy;Neogene;variational autoencoders;Metanonychus;Opiliones;Quaternary;ultraconserved elements;t-SNE;Random Forest;Laniatores

DOI：

doi:10.5061/dryad.nj2mg77

摘要：

y biology supervised machine learning has recently been used to infer species boundaries. These supervised methods require training data with associated labels. Conversely, unsupervised machine learning

Data from: Unsupervised machine learning reveals mimicry complexes in bumble bees occur along a perceptual continuum

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Biogeography;Convolutional Neural Network;coloration;Machine Learning;Müllerian mimicry;Bombus

DOI：

doi:10.5061/dryad.sd7cd06

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ross the contiguous United States and use these to quantify colour pattern mimicry using an innovative, unsupervised machine learning approach based on computer vi

Data from: Trends in anesthesiology research: a machine learning approach to theme discovery and summarization

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Natural Language Processing;Data Visualization;Machine Learning;Topic Modeling

DOI：

doi:10.5061/dryad.h86746g

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t end, we detail a pipeline, which utilizes machine learning and natural language processing for unsupervised theme extraction, and a novel method for summar

Data from: Complexity of possibly gapped histogram and analysis of histogram

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Unsupervised Machine Learning;Macrostate;Data Mechanics;statistical mechanics;Hierarchical Clustering Algorithm

DOI：

doi:10.5061/dryad.bs632

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the minimum energy macroscopic states is surprisingly resolved by applying the hierarchical clustering algorithm. Thus, a possibly gapped histogram corresponds

Data from: Study of the accuracy of a machine learning muscle MRI-based tool for diagnosis the of muscular dystrophies

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DOI：

doi:10.5061/dryad.7tb88vs

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and files containing the numeric data were generated. Random forest unsupervised machine learning was applied to develop a model useful to identify

Data from: Mirrored STDP implements autoencoder learning in a network of spiking neurons

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DOI：

doi:10.5061/dryad.kv5r4

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variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult

Data from: Integrative species delimitation reveals cryptic diversity in the southern Appalachian Antrodiaetus unicolor (Araneae: Antrodiaetidae

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DOI：

doi:10.25338/B8Z61M

摘要：

tic clustering analyses that include STRUCTURE, PCA, and a recently developed unsupervised machine learning approach (Variational Autoencoder). We evaluate