@conference {90,
	title = {Aprendizaje de independencias espec{\'\i}ficas del contexto en Markov random fields},
	booktitle = {XVII Congreso Argentino de Ciencias de la Computaci{\'o}n},
	year = {2011},
	abstract = {<p>Los modelos no dirigidos o Markov random fields son ampliamente utilizados para problemas que aprenden una distribuci{\'o}n desconocida desde un conjunto de datos. Esto es porque permiten representar una distribuci{\'o}n eficientemente al hacer expl{\'\i}citas las independencias condicionales que pueden existir entre sus variables. Adem{\'a}s de estas independencias es posible representar otras, las Independencias Espec{\'\i}ficas del Contexto (CSIs) que a diferencia de las anteriores s{\'o}lo son v{\'a}lidas bajo ciertos valores que pueden tomar subconjuntos de sus variables. Debido a esto son complicadas de representar y aprenderlas desde datos. En este trabajo presentamos un enfoque para representar CSIs en modelos no dirigidos y un algoritmo que las aprende desde datos utilizando tests estad{\'\i}sticos. Mostramos resultados donde los modelos aprendidos por nuestro algoritmo resultan ser mejores o comparables a modelos aprendidos por otros sin utilizar CSIs.</p>
},
	author = {Edera, Alejandro and Bromberg, Facundo}
}
@conference {168,
	title = {Arm muscular effort estimation from images using Computer Vision and Machine Learning},
	booktitle = {1st International Conference on Ambient Intelligence for Health},
	year = {2015},
	month = {Dec},
	address = {Puerto Varas, Chile},
	abstract = {<p>A problem of great interest in disciplines like occupational\&nbsp;medicine, ergonomics, and sports, is the measurement of biomechanical\&nbsp;variables involved in human movement and balance such as internal\&nbsp;muscle forces and joint torques. This problem \&nbsp;is solved by a two-step process: data capturing using impractical, intrusive and expensive devices\&nbsp;that is then used as input in \&nbsp;complex models for obtaining the biomechanical variables of interest. In this work we present a first step towards\&nbsp;capturing input \&nbsp;data through a more automated, non-intrusive and economic process, specifically weight held by an arm subject to isometric\&nbsp;contraction as a measure of muscular effort. We do so, by processing\&nbsp;RGB images of the arm with computer vision (Local Binary Patterns\&nbsp;and Color Histograms) and estimating the effort with machine learning\&nbsp;algorithms (SVM and Random \&nbsp;Forests). In the best case we obtained an\&nbsp;FMeasure= 70.68\%, an Accuracy= 71.66\% and a mean absolute error in\&nbsp;the predicted weights of 554.16 grs (over 3 possible levels of effort). Considering the difficulty of the problem , it is enlightening to achieve over\&nbsp;random results indicating that, despite the simplicity of the approach,\&nbsp;it is possible to extract meaningful information for the \&nbsp;predictive task.\&nbsp;Moreover, the simplicity of the approach suggests many lines of further\&nbsp;improvements: on the image capturing \&nbsp;side with other kind of images;\&nbsp;on the feature extraction side with more sophisticated algorithms and\&nbsp;features; and on the\&nbsp;knowledge extraction side with more sophisticated\&nbsp;learning algorithms.\&nbsp;</p>
},
	keywords = {color histograms, LBP, muscle arm effort, random forests, SVM},
	author = {Abraham, Leandro and Bromberg, Facundo and Forradellas, Raymundo}
}
@article {113,
	title = {An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection},
	journal = {Expert Systems with Applications},
	volume = {39},
	year = {2012},
	pages = {1822{\textendash}1829},
	author = {Catania, Carlos A and Bromberg, Facundo and Garino, Carlos Garc{\'\i}a}
}
