Accord.NET Framework

## SequentialMinimalOptimization Properties |

The SequentialMinimalOptimization type exposes the following members.

Properties

Name | Description | |
---|---|---|

ActiveExamples |
Gets the indices of the active examples (examples which have
the corresponding Lagrange multiplier different than zero).
(Inherited from BaseSequentialMinimalOptimizationTModel, TKernel, TInput.) | |

BoundedExamples |
Gets the indices of the examples at the boundary (examples
which have the corresponding Lagrange multipliers equal to C).
(Inherited from BaseSequentialMinimalOptimizationTModel, TKernel, TInput.) | |

C |
Gets or sets the cost values associated with each input vector.
(Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.) | |

CacheSize |
Gets or sets the cache size to partially store the kernel
matrix. Default is the same number of input vectors, meaning
the entire kernel matrix will be computed and cached in memory.
If set to zero, the cache will be disabled and all operations will
be computed as needed.
(Inherited from BaseSequentialMinimalOptimizationTModel, TKernel, TInput.) | |

Compact | Obsolete.
Gets or sets whether to produce compact models. Compact
formulation is currently limited to linear models.
(Inherited from BaseSequentialMinimalOptimizationTModel, TKernel, TInput.) | |

Complexity |
Complexity (cost) parameter C. Increasing the value of C forces the creation
of a more accurate model that may not generalize well. If this value is not
set and UseComplexityHeuristic is set to true, the framework
will automatically guess a value for C. If this value is manually set to
something else, then UseComplexityHeuristic will be automatically
disabled and the given value will be used instead.
(Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.) | |

Epsilon |
Epsilon for round-off errors. Default value is 1e-6.
(Inherited from BaseSequentialMinimalOptimizationTModel, TKernel, TInput.) | |

Inputs |
Gets or sets the input vectors for training.
(Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.) | |

Kernel |
Gets or sets the kernel function use to create a
kernel Support Vector Machine. If this property
is set, UseKernelEstimation will be
set to false.
(Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.) | |

Lagrange |
Gets the value for the Lagrange multipliers
(alpha) for every observation vector.
(Inherited from BaseSequentialMinimalOptimizationTModel, TKernel, TInput.) | |

Model |
Gets or sets the classifier being learned.
(Inherited from BinaryLearningBaseTModel, TInput.) | |

NegativeWeight |
Gets or sets the negative class weight. This should be a
value higher than 0 indicating how much of the Complexity
parameter C should be applied to instances carrying the negative label.
(Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.) | |

NonBoundExamples |
Gets the indices of the non-bounded examples (examples which
have the corresponding Lagrange multipliers between 0 and C).
(Inherited from BaseSequentialMinimalOptimizationTModel, TKernel, TInput.) | |

Outputs |
Gets or sets the output labels for each training vector.
(Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.) | |

PositiveWeight |
Gets or sets the positive class weight. This should be a
value higher than 0 indicating how much of the Complexity
parameter C should be applied to instances carrying the positive label.
(Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.) | |

Shrinking |
Gets or sets a value indicating whether shrinking heuristics should be used. Default is false. Note:
this property can only be used when Strategy is set to SecondOrder.
(Inherited from BaseSequentialMinimalOptimizationTModel, TKernel, TInput.) | |

Strategy |
Gets or sets the pair selection
strategy to be used during optimization.
(Inherited from BaseSequentialMinimalOptimizationTModel, TKernel, TInput.) | |

Token |
Gets or sets a cancellation token that can be used to
stop the learning algorithm while it is running.
(Inherited from BinaryLearningBaseTModel, TInput.) | |

Tolerance |
Convergence tolerance. Default value is 1e-2.
(Inherited from BaseSequentialMinimalOptimizationTModel, TKernel, TInput.) | |

UseClassProportions |
Gets or sets a value indicating whether the weight ratio to be used between
Complexity values for negative and positive instances should
be computed automatically from the data proportions. Default is false.
(Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.) | |

UseComplexityHeuristic |
Gets or sets a value indicating whether the Complexity parameter C
should be computed automatically by employing an heuristic rule.
Default is true.
(Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.) | |

UseKernelEstimation |
Gets or sets whether initial values for some kernel parameters
should be estimated from the data, if possible. Default is true.
(Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.) | |

WeightRatio |
Gets or sets the weight ratio between positive and negative class
weights. This ratio controls how much of the Complexity
parameter C should be applied to the positive class.
(Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.) |

See Also