Optimized Super Fast Support Vector Classifiers Using Python and Acceleration of RBF Computations

摘要

An optimized implementation for a parameter-less radial-basis neural paradigm called super fast vector support classifier (SFSVC) is presented. Training resides only in a process of selecting centers and consequently it is a very fast process, linear in the number of hidden neurons. Several improvements make the resulting implementation a highly effective, high speed., machine learning engine convenient for various applications on a large variety of platforms. It is shown that among various programming environments., Python with optimized math library support represents the best choice for its implementation. An important (up to 40 times) speed-up for the testing time is achieved by implementing the distance calculations as matrix multiplications., thus accelerating RBF computations on platforms with optimized math and linear algebra libraries such as Intel's MKL. Another important aspect of SFSVC., making it suitable for various embedded platforms (e.g. micro controllers., FPGA., etc.) is the lack of any tunable parameter except a unique radius., leading to a very convenient structure. The overall test plus training speed is better than for support vector machines while SFSVC has the important advantage of accepting arbitrary RBF kernels.