Optional SVM algorithm parameters.
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Constructs a parameter
- Parameters
-
| context | Context to manage the parameter of the SVM algorithm |
Constructs a parameter
- Parameters
-
| context | Context to manage the parameter of the SVM algorithm |
| kernel | Kernel function |
Constructs a parameter
- Parameters
-
| context | Context to manage the parameter of the SVM algorithm |
| kernel | Kernel function |
| c | Upper bound in constraints of the quadratic optimization problem |
Constructs a parameter
- Parameters
-
| context | Context to manage the parameter of the SVM algorithm |
| kernel | Kernel function |
| c | Upper bound in constraints of the quadratic optimization problem |
| accuracyThreshold | Accuracy of the SVM training algorithm |
Constructs a parameter
- Parameters
-
| context | Context to manage the parameter of the SVM algorithm |
| kernel | Kernel function |
| c | Upper bound in constraints of the quadratic optimization problem |
| accuracyThreshold | Accuracy of the SVM training algorithm |
| tau | Parameter of the working set selection scheme |
Constructs a parameter
- Parameters
-
| context | Context to manage the parameter of the SVM algorithm |
| kernel | Kernel function |
| c | Upper bound in constraints of the quadratic optimization problem |
| accuracyThreshold | Accuracy of the SVM training algorithm |
| tau | Parameter of the working set selection scheme |
| maxIterations | Maximal number of iterations of the SVM training algorithm |
Constructs a parameter
- Parameters
-
| context | Context to manage the parameter of the SVM algorithm |
| kernel | Kernel function |
| c | Upper bound in constraints of the quadratic optimization problem |
| accuracyThreshold | Accuracy of the SVM training algorithm |
| tau | Parameter of the working set selection scheme |
| maxIterations | Maximal number of iterations of the SVM training algorithm |
| cacheSize | Size of the cache in bytes |
Constructs a parameter
- Parameters
-
| context | Context to manage the parameter of the SVM algorithm |
| kernel | Kernel function |
| c | Upper bound in constraints of the quadratic optimization problem |
| accuracyThreshold | Accuracy of the SVM training algorithm |
| tau | Parameter of the working set selection scheme |
| maxIterations | Maximal number of iterations of the SVM training algorithm |
| cacheSize | Size of the cache in bytes |
| doShrinking | Flag that enables use of the shrinking optimization technique |
Constructs a parameter
- Parameters
-
| context | Context to manage the parameter of the SVM algorithm |
| kernel | Kernel function |
| c | Upper bound in constraints of the quadratic optimization problem |
| accuracyThreshold | Accuracy of the SVM training algorithm |
| tau | Parameter of the working set selection scheme |
| maxIterations | Maximal number of iterations of the SVM training algorithm |
| cacheSize | Size of the cache in bytes |
| doShrinking | Flag that enables use of the shrinking optimization technique |
| shrinkingStep | Number of iterations between the steps of shrinking optimization technique |
| double getAccuracyThreshold |
( |
| ) |
|
Retrieves the accuracy of the SVM training algorithm
- Returns
- Accuracy of the SVM training algorithm
Retrieves an upper bound in constraints of the quadratic optimization problem
- Returns
- Upper bound in constraints of the quadratic optimization problem
Retrieves the size of the cache in bytes to store values of the kernel matrix.
- Returns
- Size of the cache in bytes
| boolean getDoShrinking |
( |
| ) |
|
Retrieves the flag that enables use of the shrinking optimization technique
- Returns
- Flag that enables use of the shrinking optimization technique
| long getMaxIterations |
( |
| ) |
|
Retrieves the maximal number of iterations of the SVM training algorithm
- Returns
- Maximal number of iterations of the SVM training algorithm
| long getShrinkingStep |
( |
| ) |
|
Retrieves the number of iterations between the steps of shrinking optimization technique
- Returns
- Number of iterations between the steps of shrinking optimization technique
Retrieves the tau parameter of the working set selection scheme
- Returns
- Parameter of the working set selection scheme
| void setAccuracyThreshold |
( |
double |
accuracyThreshold | ) |
|
Sets the accuracy of the SVM training algorithm
- Parameters
-
| accuracyThreshold | Accuracy of the SVM training algorithm |
Sets an upper bound in constraints of the quadratic optimization problem
- Parameters
-
| C | Upper bound in constraints of the quadratic optimization problem |
| void setCacheSize |
( |
long |
cacheSize | ) |
|
Sets the size of the cache in bytes to store values of the kernel matrix. A non-zero value enables use of a cache optimization technique
- Parameters
-
| cacheSize | Size of the cache in bytes |
| void setDoShrinking |
( |
boolean |
doShrinking | ) |
|
Sets the flag that enables use of the shrinking optimization technique
- Parameters
-
| doShrinking | Flag that enables use of the shrinking optimization technique |
Sets the kernel function
- Parameters
-
| void setMaxIterations |
( |
long |
maxIterations | ) |
|
Sets the maximal number of iterations of the SVM training algorithm
- Parameters
-
| maxIterations | Maximal number of iterations of the SVM training algorithm |
| void setShrinkingStep |
( |
long |
shrinkingStep | ) |
|
Sets the number of iterations between the steps of shrinking optimization technique
- Parameters
-
| shrinkingStep | Number of iterations between the steps of shrinking optimization technique |
| void setTau |
( |
double |
tau | ) |
|
Sets the tau parameter of the working set selection scheme
- Parameters
-
| tau | Parameter of the working set selection scheme |
The documentation for this class was generated from the following file: