Statements (56)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:model
supervised learning method |
| gptkbp:abbreviation |
gptkb:SVM
|
| gptkbp:advantage |
effective in high dimensional spaces
memory efficient robust to overfitting (with proper regularization) |
| gptkbp:canUseKernel |
gptkb:algebra
linear sigmoid radial basis function |
| gptkbp:hasConcept |
hyperplane
kernel trick support vectors maximizing margin between classes |
| https://www.w3.org/2000/01/rdf-schema#label |
Support Vector Machine
|
| gptkbp:implementedIn |
gptkb:TensorFlow
gptkb:LIBSVM gptkb:Weka gptkb:scikit-learn R |
| gptkbp:introduced |
gptkb:Alexey_Chervonenkis
gptkb:Vladimir_Vapnik |
| gptkbp:introducedIn |
1963
|
| gptkbp:limitation |
difficult to interpret
not suitable for very large datasets sensitive to feature scaling |
| gptkbp:mathematicalFoundation |
learning theory
convex optimization |
| gptkbp:parameter |
gamma
C (regularization parameter) degree (for polynomial kernel) kernel type |
| gptkbp:relatedTo |
gptkb:Lagrange_multipliers
binary classification regularization outlier detection dual problem quadratic programming hard margin multiclass classification nonlinear classification soft margin structural risk minimization one-class SVM C-SVM nu-SVM |
| gptkbp:usedFor |
gptkb:dictionary
regression |
| gptkbp:usedIn |
bioinformatics
image recognition handwriting recognition face detection text classification |
| gptkbp:bfsParent |
gptkb:Text_Classification
gptkb:Supervised_learning |
| gptkbp:bfsLayer |
6
|