SVM ensembles are better when different kernel types are combined. In Berthold Lausen, editor, European Conference on Data Analysis (ECDA13). GfKl, 2013. URL http://www.gm.fh-koeln.de/~konen/ Publikationen/storkECDA-2013.pdf.Stork, J.; Ramos, R.; Koch, P.; Konen, W.: SVM ...
to a large extent, by a specific combination of differentially expressed genes. Clusters of neurons in transcriptomic space correspond to distinct cell types and in some cases—for example,Caenorhabditis elegansneurons1and retinal ganglion cells2,3,4—have been shown to share morphology and function....
Learn about machine learning models: what types of machine learning models exist, how to create machine learning models with MATLAB, and how to integrate machine learning models into systems. Resources include videos, examples, and documentation covering
Given the versatility of GET across diverse platforms and measurements, we examined its capacity for zero-shot prediction of expression-driving regulatory elements in unseen cell types. Lentivirus-based massively parallel reporter assay (lentiMPRA) provides a robust mechanism to test the regulatory activi...
Each device object has adevice type, which is stored in theDeviceTypemember of itsDEVICE_OBJECTstructure. The device type represents the type of underlying hardware for the driver. Every kernel-mode driver that creates a device object must specify an appropriate device type value when callingIoCrea...
Each device object has adevice type, which is stored in theDeviceTypemember of itsDEVICE_OBJECTstructure. The device type represents the type of underlying hardware for the driver. Every kernel-mode driver that creates a device object must specify an appropriate device type value when callingIoCrea...
load ionosphere Mdl = fitcsvm(X,Y,'KernelFunction','gaussian'); Mdl is a ClassificationSVM model. Save Model Save the SVM classification model to the file myMdl.mat by using saveLearnerForCoder. saveLearnerForCoder(Mdl,'myMdl'); Define Fixed-Point Data Types Use generateLearnerDataTypeFcn ...
Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell
the RBF kernel function is processed with higher accuracy than the polynomial and Sigmoid kernel functions, and has fewer parameters to be determined (penalization factor C, kernel parameter γ). For these reasons, RBF was chosen as the kernel function to build the SVM regression model. The same...
We combined tetrode spike data across different sessions and predicted the target (the lick port presented during the sample phase) based on delay-period neuronal ensemble activity using the support vector machine (SVM; “fitcsvm” function of MATLAB using the linear kernel, Mathworks Inc., Natick...