Optional SVM algorithm parameters.
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◆ Parameter() [1/9]
Constructs a parameter
- Parameters
-
context | Context to manage the parameter of the SVM algorithm |
◆ Parameter() [2/9]
Constructs a parameter
- Parameters
-
context | Context to manage the parameter of the SVM algorithm |
kernel | Kernel function |
◆ Parameter() [3/9]
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 |
◆ Parameter() [4/9]
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 |
◆ Parameter() [5/9]
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 |
◆ Parameter() [6/9]
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 |
◆ Parameter() [7/9]
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 |
◆ Parameter() [8/9]
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 |
◆ Parameter() [9/9]
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 |
◆ getAccuracyThreshold()
double getAccuracyThreshold |
( |
| ) |
|
Retrieves the accuracy of the SVM training algorithm
- Returns
- Accuracy of the SVM training algorithm
◆ getC()
Retrieves an upper bound in constraints of the quadratic optimization problem
- Returns
- Upper bound in constraints of the quadratic optimization problem
◆ getCacheSize()
Retrieves the size of the cache in bytes to store values of the kernel matrix.
- Returns
- Size of the cache in bytes
◆ getDoShrinking()
boolean getDoShrinking |
( |
| ) |
|
Retrieves the flag that enables use of the shrinking optimization technique
- Returns
- Flag that enables use of the shrinking optimization technique
◆ getMaxIterations()
long getMaxIterations |
( |
| ) |
|
Retrieves the maximal number of iterations of the SVM training algorithm
- Returns
- Maximal number of iterations of the SVM training algorithm
◆ getShrinkingStep()
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
◆ getTau()
Retrieves the tau parameter of the working set selection scheme
- Returns
- Parameter of the working set selection scheme
◆ setAccuracyThreshold()
void setAccuracyThreshold |
( |
double |
accuracyThreshold | ) |
|
Sets the accuracy of the SVM training algorithm
- Parameters
-
accuracyThreshold | Accuracy of the SVM training algorithm |
◆ setC()
Sets an upper bound in constraints of the quadratic optimization problem
- Parameters
-
C | Upper bound in constraints of the quadratic optimization problem |
◆ setCacheSize()
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 |
◆ setDoShrinking()
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 |
◆ setKernel()
Sets the kernel function
- Parameters
-
◆ setMaxIterations()
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 |
◆ setShrinkingStep()
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 |
◆ setTau()
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: