* Difficulty Level :Easy
* Last Updated : 22 Dec, In this article, we are going to discuss the support vector machine in machine learning. We will also cover the advantages and disadvantages and application for the same. Let’s discuss one by one.
Support Vector Machines :
Support vector machine is a supervised learning system and used for classification and regression problems. Support vector machine is extremely favored by many as it produces notable correctness with less computation power. It is mostly used in classification problems. We have three types of learning supervised, unsupervised, and reinforcement learning. A support vector machine is a selective classifier formally defined by dividing the hyperplane.
Given labeled training data the algorithm outputs best hyperplane which classified new examples. In two-dimensional space, this hyperplane is a line splitting a plane into two parts where each class lies on either side. The intention of the support vector machine algorithm is to find a hyperplane in an N-dimensional space that separately classifies the data points.
Advantages of support vector machine :
* Support vector machine works comparably well when there is an understandable margin of dissociation between classes.
* It is more productive in high dimensional spaces.
* It is effective in instances where the number of dimensions is larger than the number of specimens.
* Support vector machine is comparably memory systematic.
Disadvantages of support vector machine :
* Support vector machine algorithm is not acceptable for large data sets.
* It does not execute very well when the data set has more sound i.e. target classes are overlapping.
* In cases where the number of properties for each data point outstrips the number of training data specimens, the support vector machine will underperform.
* As the support vector classifier works by placing data points, above and below the classifying hyperplane there is no probabilistic clarification for the classification.
Applications of support vector machine :
1. Face observation –
It is used for detecting the face according to the classifier and model. . Text and hypertext arrangement –
In this, the categorization technique is used to find important information or you can say required information for arranging text. . Grouping of portrayals –
It is also used in the Grouping of portrayals for grouping or you can say by comparing the piece of information and take an action accordingly. . Bioinformatics –
In is also used for medical science as well like in laboratory, DNA, research, etc. . Handwriting remembrance –
In this, it is used for handwriting recognization. . Protein fold and remote homology spotting –
It is used for spotting or you can say the classification class into functional and structural classes given their amino acid sequences. It is one of the problems in bioinformatics. . Generalized predictive control(GPC) –
It is also used for Generalized predictive control(GPC) for predicting and it relies on predictive control using a multilayer feed-forward network as the plants linear model is presented.