## Developer Guide and Reference

• 2021.6
• 04/11/2022
• Public Content
Contents

# Support Vector Machine Classifier

Support Vector Machine Classifier is also available with oneAPI interfaces:
Support Vector Machine (SVM) is among popular classification algorithms. It belongs to a family of generalized linear classification problems. Because SVM covers binary classification problems only in the multi-class case, SVM must be used in conjunction with multi-class classifier methods. SVM is a binary classifier. For a multi-class case, use Multi-Class Classifier framework of the library.

## Details

Given feature vectors of size 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.
Training Stage
oneDAL provides two methods to train the SVM model:
The SVM model is trained to solve the quadratic optimization problem
with , , , where is the vector of ones, is the upper bound of the coordinates of the vector , is a symmetric matrix of size with , and is a kernel function.
Working subset of α updated on each iteration of the algorithm is based on the Working Set Selection (WSS) 3 scheme [Fan05]. The scheme can be optimized using one of these techniques or both:
• Cache
: the implementation can allocate a predefined amount of memory to store intermediate results of the kernel computation.
• Shrinking
: the implementation can try to decrease the amount of kernel related computations (see [Joachims99]).
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 is a bias. Each non-zero is called a classification coefficient and the corresponding is called a support vector.
Prediction Stage
Given the SVM classifier and 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

To build a Support Vector Machine (SVM) Classifier model using methods of the Model Builder class of SVM Classifier, complete the following steps:
• 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).
If after calling the getModel method you use the
setBias
,
setSupportVectors
,
setClassificationCoefficients
, or
setSupportIndices
methods, coefficients, the initial model will be automatically updated with the new set of parameters.
Examples
C++ (CPU)
Java*
There is no support for Java on GPU.
Python*

## Batch Processing

SVM classifier follows the general workflow described in Classification Usage Model.
Training
For a description of the input and output, refer to Usage Model: Training and Prediction.
At the training stage, SVM classifier has the following parameters:
Training Parameters for Support Vector Machine Classifier (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:
For GPU:
nClasses
The number of classes.
C
The upper bound in conditions of the quadratic optimization problem.
accuracyThreshold
The training accuracy.
tau
Tau parameter of the WSS scheme.
maxIterations
Maximal number of iterations for the algorithm.
cacheSize
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.
Prediction
For a description of the input and output, refer to Usage Model: Training and Prediction.
At the prediction stage, SVM classifier has the following parameters:
Prediction Parameters for Support Vector Machine Classifier (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
Performance-oriented computation method, the only prediction method supported by the algorithm.
nClasses
The number of classes.
kernel
Pointer to object of the
KernelIface
class
The kernel function. By default, the algorithm uses a linear kernel.
Examples
oneAPI DPC++
Batch Processing:
oneAPI C++
Batch Processing:
C++ (CPU)
Batch Processing:
Java*
There is no support for Java on GPU.
Batch Processing:
Python* with DPC++ support
Batch Processing:
Python*
Batch Processing:

## Performance Considerations

For the best performance of the SVM classifier, use homogeneous numeric tables if your input data set is homogeneous or SOA numeric tables otherwise.
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.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex​.
Notice revision #20201201

#### Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.