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gptkbp:instance_of
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gptkb:machine_learning
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gptkbp:analyzes
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Decision Boundary
|
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gptkbp:based_on
|
Statistical Learning Theory
|
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gptkbp:can_be_combined_with
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Ensemble Methods
|
|
gptkbp:can_be_extended_by
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Support Vector Regression
|
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gptkbp:can_be_used_to
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Polynomial Kernel
Radial Basis Function Kernel
Linear Kernel
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gptkbp:can_handle
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Non-linear data
|
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gptkbp:developed_by
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gptkb:Vladimir_Vapnik
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gptkbp:features_works_by
|
Finding hyperplane
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gptkbp:has_function
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C (Regularization parameter)
Gamma (Kernel coefficient)
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https://www.w3.org/2000/01/rdf-schema#label
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Support Vector Machines
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gptkbp:is_challenged_by
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gptkb:Hyperparameter_Tuning
Computational Complexity
Overfitting
Underfitting
Class Imbalance
Interpretability
Noise in Data
Feature Selection
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|
gptkbp:is_evaluated_by
|
Accuracy
F1 Score
Grid Search
Precision
Recall
Cross-validation
|
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gptkbp:is_implemented_in
|
gptkb:MATLAB
gptkb:LIBSVM
gptkb:Scikit-learn
|
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gptkbp:is_popular_in
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gptkb:Biology
Bioinformatics
Image Recognition
|
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gptkbp:is_related_to
|
gptkb:Decision_Trees
gptkb:Random_Forests
gptkb:neural_networks
|
|
gptkbp:is_trained_in
|
Labeled Data
|
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gptkbp:is_used_in
|
gptkb:Natural_Language_Processing
gptkb:advertising
gptkb:robotics
Finance
Healthcare
Manufacturing
|
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gptkbp:requires
|
Feature scaling
|
|
gptkbp:security
|
High-dimensional data
|
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gptkbp:sensitivity
|
gptkb:Outliers
|
|
gptkbp:suitable_for
|
Large Datasets
|
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gptkbp:used_for
|
gptkb:Regression
Classification
|
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gptkbp:uses
|
Kernel Trick
|
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gptkbp:bfsParent
|
gptkb:machine_learning
|
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gptkbp:bfsLayer
|
3
|