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
E. Spyromitros-Xioufis, S. Papadopoulos, I. Kompatsiaris, G. Tsoumakas, I. Vlahavas, “A Comprehensive Study over VLAD and Product Quantization in Large-scale Image Retrieval”, IEEE Transactions on Multimedia, IEEE, 2014.
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I. Kavakiotis, A. Triantafyllidis, P. Samaras, A. Voulgaridis, N. Karaiskou, E. Konstantinidis, I. Vlahavas, “Pattern discovery for microsatellite genome analysis”, Computers in Biology and Medicine, Edward John Ciaccio (Ed.), Elsevier, 2014.
K. Kravari, N. Bassiliades, C. Papavasileiou, “Choreographing agent encounters in the Semantic Web using rules”, Journal of Intelligent and Fuzzy Systems, IOS Press, Vol. 27, No. 2, pp. 625-640, 2014.
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C. Liaskos, G. Koutitas, I. Vlahavas, “A holistic virtual laboratory on wireless communications and sensor networks”, Journal of Interactive Mobile Technologies, International Association of Online Engineering, 2013.
A. Fachantidis, I. Partalas, G. Tsoumakas, I. Vlahavas, “Transferring Task Models in Reinforcement Learning Agents”, Neurocomputing, Elsevier, 107, pp. 23-32, 2013.
A. Tsadiras, N. Bassiliades, “RuleML Representation and Simulation of Fuzzy Cognitive Maps”, Expert Systems with Applications, Elsevier, 40 (5), pp. 1413-1426, 2013.
E. Myrgioti, N. Bassiliades, A. Miliou, “Bridging the HASM: An OWL ontology for modelling the information pathways in haptic interfaces software”, Expert Systems with Applications, Elsevier, Vol. 40, No. 4, pp. 1358-1371, 2012.
G. Meditskos, N. Bassiliades, “CLIPS-OWL: A framework for providing object-oriented extensional ontology queries in a production rule engine”, Data and Knowledge Engineering, Elsevier, 70(7), pp. 661-681, 2011.
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G. Tzanis, I. Kavakiotis, I. Vlahavas, “PolyA-iEP: A Data Mining Method for the Effective Prediction of Polyadenylation Sites”, Expert Systems with Applications, Elsevier, 38(10), pp. 1239812408, 2011.
G. Tsoumakas, E. Spyromitros-Xioufis, J. Vilcek, I. Vlahavas, “Mulan: A Java Library for Multi-Label Learning”, Journal of Machine Learning Research, 12, pp. 2411-2414, 2011.
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