Nowadays, most people use scikit-learn for machine learning projects. Because scikit-learn is a top quality ML package for Python and lets you use a machine learning algorithm in several lines of Python code, which is great!
As a machine learning researcher, I personally like to try and use other machine learning libraries. It’s good to have knowledge of other ML libraries in your arsenal. Since I used C++ for my projects, I decided to try a C++ machine learning library.
Continue reading “mlpack: A C++ machine learning library”
Recently, I’ve introduced the LightTwinSVM program on my blog (If you haven’t read it, check out this post.). It is a fast and simple implementation of TwinSVM classifier. Some people might ask why I should use this program over other popular SVM’s implementation such as LIBSVM and scikit-learn. The short answer is that TwinSVM has better accuracy than that of SVM in most cases.
In order to show the effectiveness of the LightTwinSVM program in terms of accuracy, experiments were conducted on 10 UCI datasets benchmark datasets.
Continue reading “An accuracy comparison between scikit-learn’s SVM and LightTwinSVM program”
Support Vector Machine (SVM) was proposed by Vapnik and Cortes in 1995 . It is a very popular and powerful classification algorithm. The main idea of SVM is to find an optimal separating hyperplane between two classes. Due to SVM’s great classification ability, it has been applied to a wide variety of applications.
Over the past decade, scholars have proposed classifiers on the basis of SVM. Among the extensions of SVM, I’d like to introduce Twin Support Vector Machine (TSVM) . Because it has been received more attention.
Continue reading “A Brief Introduction to Twin Support Vector Machine Classifier”