Support Vector Machine (SVM) Learning
SVM Algorithm is a supervised learning algorithm, and the way it works is by classifying data sets into different classes through a hyperplane. It marginalizes the classes and maximizes the distances between them to provide unique distinctions. We can use this algorithm for classification tasks that require more accuracy and efficiency of data.
It classifies the parts of the image as face and non-face. It contains training data of n x n pixels with a two-class face (+1) and non-face (-1). Then it extracts features from each pixel as face or non-face. Creates a square boundary around faces on the basis of pixel brightness and classifies each image by using the same process.
Text and Hypertext Categorization
Allows text and hypertext categorization for both types of models; inductive and transductive. It Uses training data to classify documents into different categories such as news articles, e-mails, and web pages
- Classification of news articles into “business” and “Movies”
- Classification of web pages into personal home pages and others
In the field of computational biology, the protein remote homology detection is a common problem. The most effective method to solve this problem is using SVM. In last few years, SVM algorithms have been extensively applied for protein remote homology detection. These algorithms have been widely used for identifying among biological sequences. For example classification of genes, patients on the basis of their genes, and many other biological problems.
We can also use SVMs to recognize hand-written characters that use for data entry and validating signatures on documents.
Generalized Predictive Control
We use SVM-based GPC to control chaotic dynamics with useful parameters. It provides excellent performance in controlling the systems. The system follows chaotic dynamics with respect to the local stabilization of the target.
Using SVMs for controlling chaotic systems has the following advantages-
- Allows use of relatively small parameter algorithms to redirect a chaotic system to the target.
- Reduces waiting time for chaotic systems.
Maintains the performance of systems