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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kevin A. Michael | |
dc.date.accessioned | 2022-05-03T09:53:36Z | - |
dc.date.available | 2022-05-03T09:53:36Z | - |
dc.date.issued | 2017 | |
dc.identifier.isbn | 9789535135715 9789535135722 | |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/147694 | - |
dc.language.iso | eng | |
dc.publisher | IntechOpen | |
dc.relation.isbasedon | 10.5772/65202 | |
dc.relation.uri | https://www.intechopen.com/books/interpreting-cardiac-electrograms-from-skin-to-endocardium | |
dc.rights.uri | CC BY-NC-ND (姓名標示-非商業性-禁止改作) | |
dc.source | DOAB | |
dc.subject.classification | Diseases of the circulatory (Cardiovascular) system | |
dc.subject.other | landslide | |
dc.subject.other | bagging ensemble | |
dc.subject.other | Logistic Model Trees | |
dc.subject.other | GIS | |
dc.subject.other | Vietnam | |
dc.subject.other | colorization | |
dc.subject.other | random forest regression | |
dc.subject.other | grayscale aerial image | |
dc.subject.other | change detection | |
dc.subject.other | gully erosion | |
dc.subject.other | environmental variables | |
dc.subject.other | data mining techniques | |
dc.subject.other | SCAI | |
dc.subject.other | GIS | |
dc.subject.other | mapping | |
dc.subject.other | single-class data descriptors | |
dc.subject.other | materia medica resource | |
dc.subject.other | Panax notoginseng | |
dc.subject.other | one-class classifiers | |
dc.subject.other | geoherb | |
dc.subject.other | change detection | |
dc.subject.other | convolutional network | |
dc.subject.other | deep learning | |
dc.subject.other | panchromatic | |
dc.subject.other | remote sensing | |
dc.subject.other | remote sensing image segmentation | |
dc.subject.other | convolutional neural networks | |
dc.subject.other | Gaofen-2 | |
dc.subject.other | hybrid structure convolutional neural networks | |
dc.subject.other | winter wheat spatial distribution | |
dc.subject.other | classification-based learning | |
dc.subject.other | real-time precise point positioning | |
dc.subject.other | convergence time | |
dc.subject.other | ionospheric delay constraints | |
dc.subject.other | precise weighting | |
dc.subject.other | landslide | |
dc.subject.other | weights of evidence | |
dc.subject.other | logistic regression | |
dc.subject.other | random forest | |
dc.subject.other | hybrid model | |
dc.subject.other | traffic CO | |
dc.subject.other | traffic CO prediction | |
dc.subject.other | neural networks | |
dc.subject.other | GIS | |
dc.subject.other | land use/land cover (LULC) | |
dc.subject.other | unmanned aerial vehicle | |
dc.subject.other | texture | |
dc.subject.other | gray-level co-occurrence matrix | |
dc.subject.other | machine learning | |
dc.subject.other | crop | |
dc.subject.other | landslide susceptibility | |
dc.subject.other | random forest | |
dc.subject.other | boosted regression tree | |
dc.subject.other | information gain | |
dc.subject.other | landslide susceptibility map | |
dc.subject.other | ALS point cloud | |
dc.subject.other | multi-scale | |
dc.subject.other | classification | |
dc.subject.other | large scene | |
dc.subject.other | coarse particle | |
dc.subject.other | particulate matter 10 (PM10) | |
dc.subject.other | landsat image | |
dc.subject.other | machine learning | |
dc.subject.other | support vector machine | |
dc.subject.other | high-resolution | |
dc.subject.other | optical remote sensing | |
dc.subject.other | object detection | |
dc.subject.other | deep learning | |
dc.subject.other | transfer learning | |
dc.subject.other | land subsidence | |
dc.subject.other | Bayes net | |
dc.subject.other | naïve Bayes | |
dc.subject.other | logistic | |
dc.subject.other | multilayer perceptron | |
dc.subject.other | logit boost | |
dc.subject.other | change detection | |
dc.subject.other | convolutional network | |
dc.subject.other | deep learning | |
dc.subject.other | panchromatic | |
dc.subject.other | remote sensing | |
dc.subject.other | leaf area index (LAI) | |
dc.subject.other | machine learning | |
dc.subject.other | Sentinel-2 | |
dc.subject.other | sensitivity analysis | |
dc.subject.other | training sample size | |
dc.subject.other | spectral bands | |
dc.subject.other | spatial sparse recovery | |
dc.subject.other | constrained spatial smoothing | |
dc.subject.other | spatial spline regression | |
dc.subject.other | alternating direction method of multipliers | |
dc.subject.other | landslide prediction | |
dc.subject.other | machine learning | |
dc.subject.other | neural networks | |
dc.subject.other | model switching | |
dc.subject.other | spatial predictive models | |
dc.subject.other | predictive accuracy | |
dc.subject.other | model assessment | |
dc.subject.other | variable selection | |
dc.subject.other | feature selection | |
dc.subject.other | model validation | |
dc.subject.other | spatial predictions | |
dc.subject.other | reproducible research | |
dc.subject.other | Qaidam Basin | |
dc.subject.other | remote sensing | |
dc.subject.other | TRMM | |
dc.subject.other | artificial neural network | |
dc.subject.other | n/a | |
dc.title | Interpreting Cardiac Electrograms - From Skin to Endocardium | |
dc.type | 電子教科書 | |
dc.classification | 醫學類 | |
Theme: | 教科書-醫學類 |
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