Statements (58)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:software_framework
|
| gptkbp:bfsLayer |
4
|
| gptkbp:bfsParent |
gptkb:Pattern_Recognition_and_Machine_Learning
|
| gptkbp:analyzes |
decision boundary
|
| gptkbp:based_on |
statistical learning theory
|
| gptkbp:can_be_extended_by |
multi-class classification
|
| gptkbp:can_be_used_with |
cross-validation
ensemble methods |
| gptkbp:challenges |
large datasets
high dimensional data |
| gptkbp:controls |
non-linear data
|
| gptkbp:developed_by |
gptkb:Vladimir_Vapnik
|
| https://www.w3.org/2000/01/rdf-schema#label |
Support vector machines
|
| gptkbp:input_output |
hyperplane
|
| gptkbp:is_evaluated_by |
gptkb:municipality
F1 score ROC curve accuracy precision AUC score |
| gptkbp:is_implemented_in |
gptkb:MATLAB
gptkb:Graphics_Processing_Unit gptkb:R gptkb:Keras gptkb:scikit-learn gptkb:WEKA gptkb:Library libsvm |
| gptkbp:is_optimized_for |
classification error
|
| gptkbp:is_part_of |
supervised learning
|
| gptkbp:is_popular_in |
image recognition
bioinformatics text classification |
| gptkbp:is_related_to |
gptkb:microprocessor
logistic regression decision trees random forests |
| gptkbp:is_used_for |
gptkb:computer
sentiment analysis spam detection anomaly detection regression polynomial kernel handwriting recognition face detection linear kernel radial basis function kernel |
| gptkbp:is_used_in |
healthcare
finance marketing |
| gptkbp:max_speed |
margin between classes
|
| gptkbp:requires |
training data
parameter tuning |
| gptkbp:security_features |
overfitting
|
| gptkbp:sensor |
outliers
|
| gptkbp:training |
stochastic gradient descent
batch gradient descent |
| gptkbp:utilizes |
kernel trick
|