Predavanje organizuju Elektrotehnički fakultet Univerziteta u Beogradu i IEEE SCG Section, CAS-SP JOINT CHAPTER
In many applications of nonlinear regression, predicting the conditional average of the target variable is not sufficient. Often, real life problems also require estimation of the uncertainty. In the first part of this talk we will explain how we applied an uncertainty analysis for an improved Earth-wide characterization of Aerosol Optical Depth (AOD) which indicates the amount of depletion that a beam of radiation undergoes as it passes through the atmosphere. Next, we will show how we used uncertainty analysis together with analysis of spatial diversity and temporal similarity to determine appropriate locations for AOD ground-based data collection sites as to maximize accuracy of AOD prediction from integrated satellite and ground-based observations. Finally, we will present a new iterative method for minimization of information loss in a budget-cut situation that requires a reduction in a number of AOD ground-based data collection sites.
Presented results are obtained in collaboration with D. Das, V. Radosavljevic, K. Ristovski and S. Vucetic at Temple University.
Zoran Obradovic, professor of computer and information sciences and the director of the Center for Information Science and Technology at Temple University in Philadelphia is an internationally recognized leader in data mining and bioinformatics. He has published more than 200 articles addressing data mining challenges in health informatics, the social sciences, environmental management and other domains. Prof. Obradovic was the program chair at five, track chair at seven and program committee member at about 40 international conferences on data mining. He currently serves as an editorial board member at seven journals.
For more details see www.ist.temple.edu/~zoran