Details

Given

n

feature vectors of size

p

and a vector of class labels , where describes the class to which the feature vector belongs, the problem is to build a two-class Support Vector Machine (SVM) classifier.

Boser method [Boser92] - performance-oriented variant of Boser [Boser92] and Platt [Platt98] algorithms
Thunder method [Wen2018]

with , , , where

e

is the vector of ones,

C

is the upper bound of the coordinates of the vector ,

Q

is a symmetric matrix of size with , and is a kernel function.

The solution of the problem defines the separating hyperplane and corresponding decision function where only those that correspond to non-zero appear in the sum, and

b

is a bias. Each non-zero is called a classification coefficient and the corresponding is called a support vector.

Given the SVM classifier and

r

feature vectors , the problem is to calculate the signed value of the decision function , . The sign of the value defines the class of the feature vector, and the absolute value of the function is a multiple of the distance between the feature vector and separating hyperplane.

Usage of Training Alternative

Create an SVM Classifier model builder using a constructor with the required number of support vectors and features.
In any sequence:
Use the
setSupportVectors
,
setClassificationCoefficients
, and
setSupportIndices
methods to add pre-calculated support vectors, classification coefficients, and support indices (optional), respectively, to the model. For each method specify random access iterators to the first and the last element of the corresponding set of values [ISO/IEC 14882:2011 § 24.2.7]_.
Use
setBias
to add a bias term to the model.
Use the
getModel
method to get the trained SVM Classifier model.
Use the
getStatus
method to check the status of the model building process. If
DAAL_NOTHROW_EXCEPTIONS
macros is defined, the status report contains the list of errors that describe the problems API encountered (in case of API runtime failure).

svm_two_class_model_builder.cpp

SVMTwoClassModelBuilder.java

svm_two_class_model_builder.py

Batch Processing

Parameter	Default Value	Description
algorithmFPType	float	The floating-point type that the algorithm uses for intermediate computations. Can be float or double .
method	defaultDense	The computation method used by the SVM classifier. Available methods for the training stage: For CPU: defaultDense – Boser method [Boser92] thunder - Thunder method [Wen2018] For GPU: thunder - Thunder method [Wen2018]
nClasses	2	The number of classes.
C	1.0	The upper bound in conditions of the quadratic optimization problem.
accuracyThreshold	0.001	The training accuracy.
tau		Tau parameter of the WSS scheme.
maxIterations	1000000	Maximal number of iterations for the algorithm.
cacheSize	8000000	The size of cache in bytes for storing values of the kernel matrix. A non-zero value enables use of a cache optimization technique.
doShrinking	true	A flag that enables use of a shrinking optimization technique. This parameter is only supported for defaultDense method.
kernel	Pointer to an object of the KernelIface class	The kernel function. By default, the algorithm uses a linear kernel.

svm_two_class_thunder_dense_batch.cpp

svm_two_class_smo_dense_batch.cpp
svm_two_class_thunder_dense_batch.cpp

svm_two_class_boser_dense_batch.cpp
svm_two_class_boser_csr_batch.cpp
svm_two_class_thunder_dense_batch.cpp
svm_two_class_thunder_csr_batch.cpp

SVMTwoClassBoserDenseBatch.java
SVMTwoClassBoserCSRBatch.java
SVMTwoClassThunderDenseBatch.java
SVMTwoClassThunderCSRBatch.java

svm_batch.py

svm_batch.py

Performance Considerations

Performance of the SVM algorithm greatly depends on the cache size cacheSize. Larger cache size typically results in greater performance. For the best SVM algorithm performance, use cacheSize equal to . However, avoid setting the cache size to a larger value than the number of bytes required to store data elements because the algorithm does not fully utilize the cache in this case.