By Yagang Zhang

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Additional resources for Application of Machine Learning

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6 0 20 40 60 80 100 Number of Clusters Fig. 6. Maximum entropy of clusters in the simple k-means clustering. 2% Simple K-means 11% 12% 7% Table 3. Error rate for three traffic traces with the different classification techniques. For the Simple K-Means, we tried different values for the number of clusters. Since the algorithm could not perfectly separate the labeled instances, we labeled each cluster with the most common label. To choose the number of clusters, we performed a 10-fold cross validation on the Auckland(a) traffic trace.

After finding the best two-stage system, we search for the best three-stage system. We assume that the best three-stage system will have the same first stage as in the best twostage system calculated in the previous iteration. Now we try every classifier in the pool (except the one picked out as a first stage) as a second stage in a three stage system. We pick the one resulting in the least complex three-stage cascade. If this least complex three-stage cascade is more complex than the previously selected two-stage cascade, then the selected two-stage cascade is declared to be the best cascade and the algorithm stops.

Decina, M. (2008). An empirical study on time-correlation of GSM telephone traffic, Wireless Communications, IEEE Transactions on 7(9): 3428–3435. Bregni, S. & Jmoda, L. (2008). Accurate estimation of the Hurst parameter of long-range dependent traffic using modified Allan and Hadamard variances, Communications, IEEE Transactions on 56(11): 1900–1906. -C. -J. (2001). LIBSVM: a library for support vector machines. , Gringoli, F. & Salgarelli, L. (2007). Traffic classification through simple statistical fingerprinting, SIGCOMM Comput.

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