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TaylorGaussian Structure

Taylor approximation for the explicit Gaussian kernel.

Namespace:  Accord.Statistics.Kernels
Assembly:  Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0
Syntax
[SerializableAttribute]
public struct TaylorGaussian : ITransform, 
	IKernel, IKernel<double[]>, ICloneable, ITransform<double[]>, 
	ILinear, ILinear<double[]>, IReverseDistance, IDistance, 
	IDistance<double[]>, IDistance<double[], double[]>
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The TaylorGaussian type exposes the following members.

Constructors
  NameDescription
Public methodTaylorGaussian(Double, Int32)
Constructs a new TaylorGaussian kernel with the given sigma.
Public methodTaylorGaussian(Gaussian, Int32)
Constructs a new TaylorGaussian kernel with the given sigma.
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Properties
  NameDescription
Public propertyDegree
Gets or sets the approximation degree for this kernel. Default is 1024.
Public propertyGaussian
Gets or sets the Gaussian kernel being approximated by this Taylor expansion.
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Methods
  NameDescription
Public methodAdd
Elementwise addition of a and b, storing in result.
Public methodClone
Creates a new object that is a copy of the current instance.
Public methodCompress
Compress a set of support vectors and weights into a single parameter vector.
Public methodCreateVector
Creates an input vector from the given double values.
Public methodDistance
Computes the distance d(x,y) between points x and y.
Public methodEquals
Indicates whether this instance and a specified object are equal.
(Inherited from ValueType.)
Public methodFunction
Gaussian Kernel function.
Public methodGetHashCode
Returns the hash code for this instance.
(Inherited from ValueType.)
Public methodGetLength
Gets the number of parameters in the input vectors.
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodProduct(Double, Double, Double)
Elementwise multiplication of scalar a and vector b, storing in result.
Public methodProduct(Double, Double, Double)
Elementwise multiplication of vector a and vector b, accumulating in result.
Public methodReverseDistance
Computes the squared distance in input space between two points given in feature space.
Public methodToDouble
Converts the input vectors to a double-precision representation.
Public methodToString
Returns the fully qualified type name of this instance.
(Inherited from ValueType.)
Public methodTransform(Double)
Projects an input point into feature space.
Public methodTransform(Double)
Projects a set of input points into feature space.
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Extension Methods
  NameDescription
Public Extension MethodDistance
Computes the kernel distance for a kernel function even if it doesn't implement the IDistance interface. Can be used to check the proper implementation of the distance function.
(Defined by Tools.)
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodTo(Type)Overloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
Public Extension MethodToTOverloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
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Remarks

References:

  • Lin, Keng-Pei, and Ming-Syan Chen. "Efficient kernel approximation for large-scale support vector machine classification." Proceedings of the Eleventh SIAM International Conference on Data Mining. 2011. Available on: http://epubs.siam.org/doi/pdf/10.1137/1.9781611972818.19

See Also