Biblio

Export 66 results:
Autor Título [ Tipo(Desc)] Año
Conference Proceedings
Bromberg F, Margaritis D.  2007.  Efficient and Robust Independence-Based Markov Network Structure Discovery.. 20th International Joint Conference of Artificial Inteliigence (IJCAI). :2431-2436.
Bromberg F, Margaritis D, Honavar V.  2006.  Efficient Markov network structure discovery using independence tests. Proceedings of the SIAM Conference in Data Mining. :141--152.
Abraham L, Bromberg F, Forradellas R.  2014.  Estimación de carga muscular mediante imágenes. Argentinean Symposium of Artificial Intelligence (ASAI) - Jornadas Argentinas de Informática. :91--98.
Gandhi P, Bromberg F, Margaritis D.  2008.  Learning Markov Network Structure using Few Independence Tests.. SIAM Data Mining. :680--691.
Grünwaldt G, Pecchia M, Diedrichs ALaura, Tabacchi G, Mercado G.  2015.  Sonda de temperatura para uso agrícola. CASE Congreso Argentino de Sistemas Embebidos.
Journal Article
Catania CA, Bromberg F, Garino CGarcía.  2012.  An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection. Expert Systems with Applications. 39:1822–1829.
Schlüter F, Strappa Y, Bromberg F, Milone DH.  2017.  Blankets Joint Posterior score for learning Markov network structures . International Journal of Approximate Reasoning. https://doi.org/10.1016/j.ijar.2017.10.018
Margaritis D, Bromberg F.  2009.  Efficient Markov network discovery using particle filters. Computational Intelligence. 25(4):367–394.
Bromberg F, Margaritis D, Honavar V.  2009.  Efficient Markov network structure discovery using independence tests. Journal of Artificial Intelligence Research. 35:449–484.
Abraham L, Bromberg F, Forradellas R.  2018.  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.
Diaz CAriel, Pérez DSebastián, Miatello H, Bromberg F.  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.
Berdún L, Bromberg F.  2009.  Guest Editorial: 10th Argentinean Symposium on Artificial Intelligence (ASAI 2009). Inteligencia Artificial.. 13(44):4.
Schlüter F, Bromberg F, Edera A.  2014.  The IBMAP approach for Markov network structure learning. Annals of Mathematics and Artificial Intelligence. 72(3):197--223.
Pérez DSebastián, Bromberg F, Diaz CAriel.  2017.  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.
Bromberg F, Margaritis D.  2009.  Improving the reliability of causal discovery from small data sets using argumentation. The Journal of Machine Learning Research. 10:301–340.
Edera A, Schlüter F, Bromberg F.  2014.  Learning Markov networks networks with context-specific independences.. International Journal on Artificial Intelligence Tools. 23(06)
Watteyne T, Diedrichs ALaura, Brun-Laguna K, Chaar JEmilio, Dujovne D, Taffernaberry JCarlos, Mercado G.  2016.  Peach: Predicting frost events in peach orchards using iot technology. EAI Endorsed Transactions on the Internet of Things. (5)
Diedrichs ALaura, Bromberg F, Dujovne D, Brun-Laguna K, Watteyne T.  2018.  Prediction of frost events using machine learning and IoT sensing devices. IEEE Internet of Things Journal. 5(6):4589-4597.
Schlüter F.  2012.  A survey on independence-based Markov networks learning. Artificial Intelligence Review. 42(4):1093.
Edera AA., Gandini CL, M. Sanchez-Puerta V.  2018.  Towards A Comprehensive Picture Of C-to-U RNA Editing Sites In Angiosperm Mitochondria (under review). Plant Molecular Biology.

Páginas