Browsing by Subject "Machine learning techniques"

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Open Access
Classification of regional ionospheric disturbance based on machine learning techniques
(European Space Agency, 2016) Terzi, Merve Begüm; Arıkan, Orhan; Karatay, S.; Arıkan, F.; Gulyaeva, T.
In this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations.
Open Access
Generic text summarization for Turkish
(Oxford University Press, 2010) Kutlu, M.; Cığır, C.; Cicekli, I.
In this paper, we propose a generic text summarization method that generates summaries of Turkish texts by ranking sentences according to their scores. Sentence scores are calculated using their surface-level features, and summaries are created by extracting the highest ranked sentences from the original documents. To extract sentences which form a summary with an extensive coverage of the main content of the text and less redundancy, we use features such as term frequency, key phrase (KP), centrality, title similarity and sentence position. The sentence rank is computed using a score function that uses its feature values and the weights of the features. The best feature weights are learned using machine-learning techniques with the help of human-constructed summaries. Performance evaluation is conducted by comparing summarization outputs with manual summaries of two newly created Turkish data sets. This paper presents one of the first Turkish summarization systems, and its results are promising. We introduce the usage of KP as a surface-level feature in text summarization, and we show the effectiveness of the centrality feature in text summarization. The effectiveness of the features in Turkish text summarization is also analyzed in detail. © The Author 2008. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.