Statements (56)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:model
|
| gptkbp:canBe |
bioinformatics
diagnosis financial forecasting fraud detection pattern recognition time series prediction handwriting recognition image classification gene expression analysis sentiment analysis face detection object detection text classification customer segmentation spam detection protein classification |
| gptkbp:canHandleNonlinearData |
true
|
| gptkbp:canUseKernelTrick |
true
|
| gptkbp:category |
supervised learning
|
| gptkbp:fullName |
gptkb:Support_Vector_Machine
|
| gptkbp:hasConcept |
hyperplane
support vectors maximizing margin between classes |
| https://www.w3.org/2000/01/rdf-schema#label |
SVM
|
| gptkbp:implementedIn |
gptkb:TensorFlow
gptkb:LIBSVM gptkb:Weka gptkb:scikit-learn R |
| gptkbp:introduced |
gptkb:Alexey_Chervonenkis
gptkb:Vladimir_Vapnik |
| gptkbp:introducedIn |
1990s
|
| gptkbp:kernelTypes |
gptkb:algebra
linear sigmoid radial basis function |
| gptkbp:openSource |
gptkb:LIBSVM
gptkb:Weka gptkb:scikit-learn R |
| gptkbp:optimizationProblem |
quadratic programming
|
| gptkbp:output |
continuous values
discrete labels |
| gptkbp:relatedTo |
structural risk minimization
maximum margin classifier soft margin classifier |
| gptkbp:requiresLabeledData |
true
|
| gptkbp:sensitivity |
feature scaling
|
| gptkbp:usedFor |
gptkb:dictionary
regression outlier detection |
| gptkbp:bfsParent |
gptkb:SV_Meppen
gptkb:Support_Vector_Machines gptkb:AMD-V |
| gptkbp:bfsLayer |
6
|