Time efficiency and mistake rates for online learning algorithms : A comparison between Online Gradient Descent and Second Order Perceptron algorithm and their performance on two different data sets
Sammanfattning: This dissertation investigates the differences between two different online learning algorithms: Online Gradient Descent (OGD) and Second-Order Perceptron (SOP) algorithm, and how well they perform on different data sets in terms of mistake rate, time cost and number of updates. By studying different online learning algorithms and how they perform in different environments will help understand and develop new strategies to handle further online learning tasks. The study includes two different data sets, Pima Indians Diabetes and Mushroom, together with the LIBOL library for testing. The results in this dissertation show that Online Gradient Descent performs overall better concerning the tested data sets. In the first data set, Online Gradient Descent recorded a notably lower mistake rate. For the second data set, although it recorded a slightly higher mistake rate, the algorithm was remarkably more time efficient compared to Second-Order Perceptron. Future work would include a wider range of testing with more, and different, data sets as well as other relative algorithms. This will lead to better result and higher credibility.
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