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Logarithmic Incremental Parameter Tuning of Support Vector Machines for Human Activity Recognition
Objectives: In machine learning based human activity monitoring, the algorithm needs to produce a prediction model with a high accuracy. Support vector machine is one of the leading machine learning algorithms. Methods/Statistical Analysis: We propose an optimization approach of support vector machines that optimizes its regularization parameter to further improve its prediction accuracy in a human activity recognition application. In order to implement an efficient support vector machines predictive model of a particular dataset that would generalize well and have a good prediction performance, a suitable regularization parameter has to be applied in the regularization part of the equation. Findings: In order to empirically evaluate the effectiveness of our proposed approach, we show the results of our implementation and discuss the results of our proposed approach explained in the previous section on support vector machines models. From our experiments, we can see that we got fabulous results when the regularization parameter is 1000. For the accuracy on train/test dataset pair, we got a sufficiently high percentage for regularization parameter values of 10, 100 and 1000. And, the best cross validation accuracy is 98.8575, which is corresponding to a regularization parameter value of 1000. Additionally, we can also notice that the relation between the classification accuracy and the cross validation accuracy is proportional, and that is obvious in the accuracies responding to the regularization parameter of 0.0001, because both accuracies are significantly low. Improvements/Applications: Our idea was to replace the parameter value with a vector of parameter values and compare their results. It shows more improved and promising performance improvement but if we can apply parallel programming.
Support Vector Machines, Regularized Support Vector Machines, Wireless Sensor Network, Human Activity Recognition, and Incremental Parameter Tuning.
- Vapnik V. An Overview of Statistical Learning Theory. IEEE Transactions On Neural Networks. 1999; 10(5):988-99.
- Fletcher T. Support Vector Machines Explained. Tutorial paper-PhD. 2008; p. 1-19.
- Kearns M, Vazirani U. Park Plaza, Boston, MA: PWS publishing Company: An Introduction to Computational Theory. 1997; p. 410.
- Tibshirani R. Regression Shrinkage and Selection via the LASSO. Journal of the Royal Statistics Society, Series b (Methodological). 1996; 58(1):267-88.
- Draper N, Smith H. New York: Wiley: Applied Regression Analysis, 3rd (edn). 1998; p. 736.
- Refaeilzadeh P, Tang L, Liu H. Indian Institute of Technology: Statistical Machine Translation. Course Material. 2008.
- Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz J.Human Activity Recognition on Smartphone’s using a Multiclass Hardware-Friendly Support Vector Machine.Spain, Vitoria-Gasteiz: International Workshop of Ambient Assisted Living (IWAAL 2012). 2012 Dec; p. 1-8.
- Lee S, Lee H, Abbeel P, Ng A. Efficient L1 Regularized Logistic Regression. Massachusetts, USA: the proceeding of the national conference on artificial intelligence. 2006; p. 401-08.
- Chavarriaga R, Sagha H, Calatroni A, Digumarti S, Troster G, Millan J, Roggen D. The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition. Pattern Recognition Letters. 2013; 34(15):2033-42.
- Roggen D, Calatroni A, Rossi M, Holleczek T, Troster G, Lukowicz P, Pirkl G, Bannach D, Ferscha A, Doppler J, Holzmann C, Kurz M, Holl G, Chavarriaga R, Sagha H, Bayati H, Millan J. Collecting complex activity data sets in highly rich networked sensor environments. Kassel, Germany: Seventh International Conference on Networked Sensing Systems (INSS’10). 2010; p. 233-40.
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