SAP HANA Predictive Analysis Library (PAL)

3.2.14 Support Vector Machine
Support Vector Machines (SVMs) refer to a family of supervised learning models using the concept of support
vector. Compared with many other supervised learning models, SVMs have the advantages in that the models
produced by SVMs can be either linear or non-linear, where the latter is realized by a technique called Kernel
Trick.
Like most supervised models, there are training phase and testing phase for SVMs. In the training phase, a
function f(x):->y where f(∙) is a function (can be non-linear) mapping a sample onto a TARGET, is learnt.
The training set consists of pairs denoted by {x i , y i }, where x denotes a sample represented by several
attributes, and y denotes a TARGET (supervised information). In the testing phase, the learnt f(∙) is further
used to map a sample with unknown TARGET onto its predicted TARGET.
In the current implementation in PAL, SVMs can be used for the following three tasks:
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Support Vector Classification (SVC)
Classification is one of the most frequent tasks in many fields including machine learning, data mining,
computer vision, and business data analysis. Compared with linear classifiers like logistic regression, SVC
is able to produce non-linear decision boundary, which leads to better accuracy on some real world
dataset. In classification scenario, f(∙) refers to decision function, and a TARGET refers to a "label"
represented by a real number.
Support Vector Regression (SVR)
SVR is another method for regression analysis. Compared with classical linear regression methods like
least square regression, the regression function in SVR can be non-linear. In regression scenario, f(∙)
refers to regression function, and TARGET refers to "response" represented by a real number.
Support Vector Ranking
This implements a pairwise "learning to rank" algorithm which learns a ranking function from several sets
(distinguished by Query ID) of ranked samples. In the scenario of ranking, f(∙) refers to ranking function,
and TARGET refers to score, according to which the final ranking is made. For pairwise ranking, f(∙) is
learnt so that the pairwise relationship expressing the rank of the samples within each set is considered.
Because non-linearity is realized by Kernel Trick, besides the datasets, the kernel type and parameters should
be specified as well.
Prerequisite
No missing or null data in the inputs.
SVMTRAIN
This function reads the input data and generates training model.
Procedure Generation
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘SVMTRAIN’,
‘’, '', );
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 227