Biblio
Efficient and Robust Independence-Based Markov Network Structure Discovery.. 20th International Joint Conference of Artificial Inteliigence (IJCAI). :2431-2436.
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2007. Efficient Markov network structure discovery using independence tests. Proceedings of the SIAM Conference in Data Mining. :141--152.
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2006. Estimación de carga muscular mediante imágenes. Argentinean Symposium of Artificial Intelligence (ASAI) - Jornadas Argentinas de Informática. :91--98.
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2014. Learning Markov Network Structure using Few Independence Tests.. SIAM Data Mining. :680--691.
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2008. Multi species weed detection with Retinanet one-step network in a maize field. Precision agriculture’21. :2223–2228.
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2021. Sonda de temperatura para uso agrícola. CASE Congreso Argentino de Sistemas Embebidos.
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2015. An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection. Expert Systems with Applications. 39:1822–1829.
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2012. Blankets Joint Posterior score for learning Markov network structures . International Journal of Approximate Reasoning. https://doi.org/10.1016/j.ijar.2017.10.018
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2017. Efficient Markov network discovery using particle filters. Computational Intelligence. 25(4):367–394.
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2009. Efficient Markov network structure discovery using independence tests. Journal of Artificial Intelligence Research. 35:449–484.
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2009. Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds. Computers in Biology and Medicine. 95:129-139.
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2018. Grapevine buds detection and localization in 3D space based on Structure from Motion and 2D image classification. Computers in Industry. 99C (Special Issue on Machine Vision for Outdoor Environments):303-312.
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2018. Guest Editorial: 10th Argentinean Symposium on Artificial Intelligence (ASAI 2009). Inteligencia Artificial.. 13(44):4.
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2009. The IBMAP approach for Markov network structure learning. Annals of Mathematics and Artificial Intelligence. 72(3):197--223.
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2014. Image classification for detection of winter grapevine buds in natural conditions using scale-invariant features transform, bag of features and support vector machines. Computers and Electronics in Agriculture. 135:81-95.
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2017. Improving the reliability of causal discovery from small data sets using argumentation. The Journal of Machine Learning Research. 10:301–340.
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2009. Learning Markov networks networks with context-specific independences.. International Journal on Artificial Intelligence Tools. 23(06)
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2014. Peach: Predicting frost events in peach orchards using iot technology. EAI Endorsed Transactions on the Internet of Things. (5)
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2016. Prediction of frost events using machine learning and IoT sensing devices. IEEE Internet of Things Journal. 5(6):4589-4597.
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2018. A survey on independence-based Markov networks learning. Artificial Intelligence Review. 42(4):1093.
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2012. Towards A Comprehensive Picture Of C-to-U RNA Editing Sites In Angiosperm Mitochondria (under review). Plant Molecular Biology.
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2018. Towards practical 2D grapevine bud detection with fully convolutional networks. Computers and Electronics in Agriculture. 182:105947.
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