2. Data Science Algorithms

Support Vector Machines

Support Vector machines are powerful classifiers for classification of binary data. They are also used in facial recognition and genetic classification. SVMs have pre-built regularization model that allows data scientists to SVMs automatically minimize the classification error.

It, therefore, helps to increase the geometrical margin which is an essential part of an SVM classifier.

Support Vector Machines can map the input vectors to n-dimensional space. They do so by building a maximum separation hyperplane. SVM’s are formed by structure risk minimization.

There also two other hyperplanes, on either side of the initially constructed hyperplane. We measure the distance from the central hyperplane to the other two hyperplanes.

Leave a Reply

Your email address will not be published. Required fields are marked *