@conference {92, title = {Independence-based MAP for Markov networks structure discovery}, booktitle = {23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI)}, year = {2011}, month = {11/2013}, publisher = {IEEE}, organization = {IEEE}, author = {Bromberg, F. and Schl{\"u}ter, F. and Edera, A.} } @conference {103, title = {Interpolaci{\'o}n Espacial Mediante Aprendizaje de M{\'a}quinas en Vi{\~n}edos de la Provincia de Mendoza, Argentina.}, booktitle = {13th Argentine Symposium on Artificial Intelligence}, year = {2012}, isbn = {1850-2784}, author = {Bromberg, F and P{\'e}rez, Diego S} } @article {69, title = {The IBMAP approach for Markov network structure learning}, journal = {Annals of Mathematics and Artificial Intelligence}, volume = {72}, year = {2014}, month = {04/2014}, pages = {197--223}, chapter = {197}, abstract = {

In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum.

}, keywords = {68T05, EDAs, Independence tests, Knowledge discovery, Markov network, Structure learning}, issn = {1012-2443}, doi = {10.1007/s10472-014-9419-5}, url = {http://dx.doi.org/10.1007/s10472-014-9419-5}, author = {Schl{\"u}ter, Federico and Bromberg, Facundo and Edera, Alejandro} } @article {210, title = {Image classification for detection of winter grapevine buds in natural conditions using scale-invariant features transform, bag of features and support vector machines}, journal = {Computers and Electronics in Agriculture}, volume = {135}, year = {2017}, month = {04/2017}, pages = {81-95}, chapter = {81}, abstract = {

In viticulture, there are several applications where bud detection in vineyard images is a necessary task, susceptible of being automated through the use of computer vision methods. A common and effective family of visual detection algorithms are the scanning-window type, that slide a (usually) fixed size window along the original image, classifying each resulting windowed-patch as containing or not containing the target object. The simplicity of these algorithms finds its most challenging aspect in the classification stage. Interested in grapevine buds detection in natural field conditions, this paper presents a classification method for images of grapevine buds ranging 100 to 1600 pixels in diameter, captured in outdoor, under natural field conditions, in winter (i.e., no grape bunches, very few leaves, and dormant buds), without artificial background, and with minimum equipment requirements. The proposed method uses well-known computer vision technologies: Scale-Invariant Feature Transform for calculating low-level features, Bag of Features for building an image descriptor, and Support Vector Machines for training a classifier. When evaluated over images containing buds of at least 100 pixels in diameter, the approach achieves a recall higher than 0.9 and a precision of 0.86 over all windowed-patches covering the whole bud and down to 60\% of it, and scaled up to window patches containing a proportion of 20\%-80\% of bud versus background pixels. This robustness on the position and size of the window demonstrates its viability for use as the classification stage in a scanning-window detection algorithms.

}, keywords = {computer vision, Grapevine bud, Image classification, Precision viticulture, Scanning-window detection}, issn = {0168-1699}, doi = {10.1016/j.compag.2017.01.020}, url = {http://www.sciencedirect.com/science/article/pii/S0168169916301818}, author = {P{\'e}rez, Diego Sebasti{\'a}n and Bromberg, Facundo and Diaz, Carlos Ariel} } @article {172, title = {Improving the reliability of causal discovery from small data sets using argumentation}, journal = {The Journal of Machine Learning Research}, volume = {10}, year = {2009}, month = {02/2009}, pages = {301{\textendash}340}, abstract = {

We address the problem of improving the reliability of independence-based causal discovery algorithms that results from the execution of statistical independence tests on small data sets, which typically have low reliability. We model the problem as a knowledge base containing a set of independence facts that are related through Pearl{\textquoteright}s well-known axioms. Statistical tests on finite data sets may result in errors in these tests and inconsistencies in the knowledge base. We resolve these inconsistencies through the use of an instance of the class of defeasible logics called argumentation, augmented with a preference function, that is used to reason about and possibly correct errors in these tests. This results in a more robust conditional independence test, called an argumentative independence test. Our experimental evaluation shows clear positive improvements in the accuracy of argumentative over purely statistical tests. We also demonstrate significant improvements on the accuracy of causal structure discovery from the outcomes of independence tests both on sampled data from randomly generated causal models and on real-world data sets.

}, keywords = {argumentation, causal Bayesian netw orks, Independence-based causal discovery, reliability improvement, Structure learning}, url = {http://www.jmlr.org/papers/v10/bromberg09a.html}, author = {Bromberg, Facundo and Margaritis, Dimitris} } @article {40, title = {ImageNets}, year = {2010}, url = {http://imagenets.sourceforge.net/} }