LIBSVM is an open-source ML (Machine learning) library. This is developed at National Taiwan University and written in C++ language along with a C (API). It helps in implementing the SMO (Sequential minimal optimization) algorithm for SVMs (support vector machines) and also supports regression and classification
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SVM or Support vector machine is a set of methods including supervised learning which is used for regression, classification, and outliers’ detection. It can be used in the detection of cancerous cells based on countless images or can be utilized for predicting driving routes for the future with an applicable model of regression.
Specific types of SVMs are useful in supporting specific problems of machine learning. For example, SVR (support vector regression) is an extension of SVC (support vector classification). So, it must be very clear to you that these are nothing but math equations that are tuned up efficiently for providing the most accurate and quick answer as soon as possible. Find LIBSVM Developers and hire freelancers in Pakistan, India, China, Canada, the UK, the US, the Philippines, and all over the world from Paperub.
However, SVMs are much different than any other algorithms of classification because they choose the decision boundary in such a way that maximizes or increases the interspace of the accessible data points of multiple classes. This SVM-created decision boundary is known as the maximum margin hyperplane or maximum margin classifier.
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LIBSVM offers a simple interface to help users from various other fields to use the tool SVM easily. The simple interface of SVM helps the users in linking it with their programs quickly. Following are the major features of LIBSVM that are depicted below:
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There are two different variants of SVMs that are used for distinctive things:
There are various benefits of using LIBSVM but here we have briefly mentioned the important benefits of the same:
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