Browsing by Subject "Support vector machine"

Now showing 1 - 3 of 3

Open Access
A novel distributed anomaly detection algorithm based on support vector machines
(Elsevier, 2020-01) Ergen, Tolga; Kozat, Süleyman S.
In this paper, we study anomaly detection in a distributed network of nodes and introduce a novel algorithm based on Support Vector Machines (SVMs). We first reformulate the conventional SVM optimization problem for a distributed network of nodes. We then directly train the parameters of this SVM architecture in its primal form using a gradient based algorithm in a fully distributed manner, i.e., each node in our network is allowed to communicate only with its neighboring nodes in order to train the parameters. Therefore, we not only obtain a high performing anomaly detection algorithm thanks to strong modeling capabilities of SVMs, but also achieve significantly reduced communication load and computational complexity due to our fully distributed and efficient gradient based training. Here, we provide a training algorithm in a supervised framework, however, we also provide the extensions of our implementation to an unsupervised framework. We illustrate the performance gains achieved by our algorithm via several benchmark real life and synthetic experiments.
Open Access
Prediction of cryptocurrency returns using machine learning
(Springer, 2021-02) Akyildirim, E.; Goncu, A.; Sensoy, Ahmet
In this study, the predictability of the most liquid twelve cryptocurrencies are analyzed at the daily and minute level frequencies using the machine learning classification algorithms including the support vector machines, logistic regression, artificial neural networks, and random forests with the past price information and technical indicators as model features. The average classification accuracy of four algorithms are consistently all above the 50% threshold for all cryptocurrencies and for all the timescales showing that there exists predictability of trends in prices to a certain degree in the cryptocurrency markets. Machine learning classification algorithms reach about 55–65% predictive accuracy on average at the daily or minute level frequencies, while the support vector machines demonstrate the best and consistent results in terms of predictive accuracy compared to the logistic regression, artificial neural networks and random forest classification algorithms.
Open Access
Surveillance using both video and audio
(Springer, 2008) Dedeoğlu, Yiğithan; Töreyin, B. Uğur; Güdükbay, Uğur; Çetin, A. Enis; Maragos, P.; Potamianos, A.; Gros, P.
It is now possible to install cameras monitoring sensitive areas but it may not be possible to assign a security guard to each camera or a set of cameras. In addition, security guards may get tired and watch the monitor in a blank manner without noticing important events taking place in front of their eyes. Current CCTV surveillance systems are mostly based on video and recently intelligent video analysis systems capable of detecting humans and cars were developed for surveillance applications. Such systems mostly use Hidden Markov Models (HMM) or Support Vector Machines (SVM) to reach decisions. They detect important events but they also produce false alarms. It is possible to take advantage of other low cost sensors including audio to reduce the number of false alarms. Most video recording systems have the capability of recording audio as well. Analysis of audio for intelligent information extraction is a relatively new area. Automatic detection of broken glass sounds, car crash sounds, screams, increasing sound level at the background are indicators of important events. By combining the information coming from the audio channel with the information from the video channels, reliable surveillance systems can be built. In this chapter, current state of the art is reviewed and an intelligent surveillance system analyzing both audio and video channels is described.