A REFERENCE REVIEW ON MACHINE LEARNING TOOLKIT

Abstract

There are many great toolkits that provide support for developing machine learning software in Python, R, Matlab, and similar environments. Dlibml is an open source library for both engineers and researchers, with the goal of providing an equally rich environment for developing machine learning software in the C ++ language. To this end, dlibml includes an extensible linear algebra toolkit with built-in BLAS support. It also includes the implementation of algorithms for performing Bayesian network inference and kernel-based methods for classification, regression, clustering, anomaly detection, and feature ranking. To make these tools easy to use, the entire custom programming library has been developed to provide complete, concise documentation and powerful debugging tools. Keywords: kernel method, svm, rvm, kernel clustering, C ++, Bayesian network.