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Data from: Mie scattering and microparticle based characterization of heavy metal ions and classification by statistical inference methods
- 负责人：
- DOI：
- doi:10.5061/dryad.62n8p0q
- 摘要：
- linear discriminant analysis, support vector machine analysis, K-means clustering, and K-medians clustering. This study found the highest classification accuracy usi
Data from: Phylogeography and support vector machine classification of colour variation in panther chameleons
- 负责人：
- DOI：
- doi:10.5061/dryad.74b7h
- 摘要：
- ng a supervised multiclass support vector machine approach on five anatomical components, we identify patterns in 3D colour space that efficiently predict
Data from: Automatic supporting system for regionalization of ventricular tachycardia exit site in implantable defibrillators
- 负责人：
- DOI：
- doi:10.5061/dryad.nm0v0
- 摘要：
- tworks (NN), and Support Vector Machines (SVM)) and regression (Kernel Ridge Regression) problem statements. Classifiers were evaluated by using accurac
Data from: Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping
- 负责人：
- DOI：
- doi:10.5061/dryad.1m8tg17
- 摘要：
- relationship) to evaluate the performance of four machine learning (ML) classifiers (classification trees, random forests, support vector machines
Data from: A novel biomechanical approach for animal behaviour recognition using accelerometers
- 负责人：
- Chakravarty, Pritish
- 关键词：
- DOI：
- doi:10.5061/dryad.7q294p8
- 摘要：
- ) and (c) when data from new individuals were considered (LOIO). A linear‐kernel Support Vector Machine at each node of our classification scheme yielded an ove
Data from: Automated taxonomic identification of insects with expert-level accuracy using effective feature transfer from convolutional networks
- 负责人：
- 关键词：
- DOI：
- doi:10.5061/dryad.20ch6p5
- 摘要：
- pretrained on the ImageNet dataset. This information is fed into a linear support vector machine classifier, which is trained on the target problem. We tested
Data from: Automatic recognition of self-acknowledged limitations in clinical research literature
- 负责人：
- DOI：
- doi:10.5061/dryad.06ds7
- 摘要：
- the training set in order to improve classification performance. The machine learning algorithms used were logistic regression (LR) and support vector machines
Data from: Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart
- 负责人：
- DOI：
- doi:10.5061/dryad.3f9r8r6
- 摘要：
- t moderate classification accuracy can be achieved to predict ventricular tachyarrhythmia with machine learning algorithms using HRV features from ICD data
Data from: Classifying three imaginary states of the same upper extremity using time-domain features
- 负责人：
- 关键词：
- DOI：
- doi:10.5061/dryad.6qs86
- 摘要：
- and classifies them using a support vector machine (SVM) with a radial basis kernel function (RBF). An average accuracy of 74.2% was obtained when usi
Data from: Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer
- 负责人：
- DOI：
- doi:10.5061/dryad.m5n98
- 摘要：
- , the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms
