Pisístrato y el episodio del carro de Atenea. Nueva propuesta de datación
Resumen
El episodio de Pisístrato volviendo en un carro junto a Atenea es uno de los acontecimientos mejor conocidos del período griego arcaico. Siguiendo sobre todo a Heródoto y a Aristóteles, la historiografía moderna actual lo sitúa tras su primer exilio y antes de la consecución de su segunda tiranía. Esta consideración plantea una serie de problemas, como el papel de Atenea en un acuerdo prenupcial. Por ello, a través de un estudio pormenorizado de las fuentes, así como del contexto histórico, proponemos aplazar esta entrada teatralizada al 546, cuando se produce la batalla de Palene y poco antes de que Pisístrato se instaure como tirano por tercera y última vez. Esta nueva cronología asienta mejor algunas interpretaciones que se han venido dando, pero también permite, a su vez, novedosas lecturas sobre los medios de legitimación religiosos y sociales empleados por el tirano de Atenas.
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Elmas, Ç., 2003. Yapay Sinir Ağları (Kuram, Mimari, Eğitim, Uygulama). Ankara: Seçkin Yayıncılık.
Harrington, P.,2012. Machine learning in action. Shelter Island, NY: Manning Publications Co.
Hamoud, A. A., Alwehaibi, A., Roy, K., and Bikdash, M. 2018. Classifying political tweets using Naïve Bayes and support vector machines. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems(736-744). Springer, Cham.
Huq, M. R., Ali, A., and Rahman, A., 2017. Sentiment analysis on Twitter data using KNN and SVM. (IJACSA) International Journal of Advanced Computer Science and Applications, 8(6): 19-25.
Kayikci, S., Akyazi, E., 2018. Classification of Open Directory Web Pages Using Artificial Neural Networks. International Journal of Scientific and Technological Research, 2422-8702
Kaynar, O., Görmez, Y., Yıldız, M., and Albayrak, A., 2016 .Makine öğrenmesi yöntemleri ile Duygu Analizi. In International Artificial Intelligence and Data Processing Symposium (IDAP’16), 17-18.
Kotzias, D., Denil, M., De Freitas, N., and Smyth, P. 2015. From group to individual labels using deep features. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 597-606.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J., 2013. Distributed compositionality. Advances in Neural Information Processing Systems. 26: 3111-3119.
Nikfarjam, A, Sarker, A, O’Connor, K, Ginn, R, and Gonzalez, G., 2015. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features, Journal of the American Medical Informatics Association, 22(3): 671-681
Nizam, H, Akın, S. S.,2014. Sosyal medyada makine öğrenmesi ile duygu analizinde dengeli ve dengesiz veri setlerinin performanslarının karşılaştırılması. XIX. Türkiye’de İnternet Konferansı.
Pang, B., Lee, L., and Vaithyanathan, S. 2002. Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070.
Rana, S. and Singh, A.,2016. Comparative analysis of sentiment orientation using SVM and Naïve Bayes techniques, 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, pages 106-111, doi: 10.1109/NGCT.2016.7877399.
Rogers, R., 2014. Debanalising Twitter. Twitter and Society, New York, NY, ix-xxxviii.
Sjögren, R., Stridh, K., Skotare, T., and Trygg, J., 2020. Multivariate patent analysis–Using chemometrics to analyze collections of chemical and pharmaceutical patents. Journal of Chemometrics, 34(1): e3041.
Song, O., Hu, W., and Xie, W, 2002. Robust Support Vector Machine with Bullet Hole Image Classification, IEEE Transactions on Systems, Man and Cybernetics – Part C: Applications and Rewiews, 32(4): 440-448.
Symeonidis S, Effrosynidis D., and Arampatzis A., 2002. A comparative evaluation of pre‐processing techniques and their interactions for Twitter sentiment analysis. Expert System Applications, 110:298‐310.
Türkmen, A. C. Cemgil, A. T., 2014. Political interest and tendency prediction from microblog data. In: 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 1327-1330
Wright, G., Rodriguez, A., Li, J., Clark, P. L., Milenković, T., and Emrich, S. J., 2020. Analysis of computational codon usage models and their association with translationally slow codons. PloS one, 15(4): e0232003.
Xiao, C., Xia, W., and Jiang, J., 2020. Stock price forecast based on combined model of ARI-MA-LS-SVM. Neural Computing and Applications, 1-10.
Pisístrato y el episodio del carro de Atenea. Nueva propuesta de datación. (2021). Studia Historica. Historia Antigua, 39, 51-72. https://doi.org/10.14201/shha2021395172
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