Classical Support Vector Machines
GPTKB entity
Statements (51)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:software_framework
|
| gptkbp:analyzes |
Decision Boundary
Support Vectors Geometric Margin |
| gptkbp:applies_to |
Binary Classification
Multi-class Classification |
| gptkbp:based_on |
Statistical Learning Theory
|
| gptkbp:benefits |
Computationally Intensive
Effective in High Dimensional Spaces Requires Careful Parameter Tuning |
| gptkbp:can_be_extended_by |
Support Vector Regression
One-class SVM |
| gptkbp:developed_by |
gptkb:Alexey_Chervonenkis
gptkb:Vladimir_Vapnik |
| https://www.w3.org/2000/01/rdf-schema#label |
Classical Support Vector Machines
|
| gptkbp:is_evaluated_by |
F1 Score
Cross-validation Precision and Recall Accuracy Metrics |
| gptkbp:is_implemented_in |
Various Software Libraries
|
| gptkbp:is_optimized_for |
Structural Risk
|
| gptkbp:is_popular_in |
gptkb:Pattern_Recognition
gptkb:computer Bioinformatics Data Mining Image Classification |
| gptkbp:is_related_to |
gptkb:Artificial_Intelligence
gptkb:software_framework Optimization Statistical Learning |
| gptkbp:is_used_for |
gptkb:Regression
Classification |
| gptkbp:is_used_in |
gptkb:film_production_company
gptkb:Telecommunications_company Finance Healthcare Manufacturing |
| gptkbp:max_speed |
Margin
|
| gptkbp:requires |
Training Data
Feature Scaling |
| gptkbp:sensor |
gptkb:Outliers
|
| gptkbp:training |
Computer Science Programs
Data Science Programs Statistics Courses Machine Learning Courses Artificial Intelligence Courses |
| gptkbp:type |
gptkb:software_framework
|
| gptkbp:uses |
Kernel Trick
Hyperplanes |
| gptkbp:bfsParent |
gptkb:Quantum_Support_Vector_Machines
|
| gptkbp:bfsLayer |
3
|