@article {18903,
	title = {Evolution of state-dependent risk preferences},
	journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},
	volume = {1},
	year = {2010},
	month = {2010/10//},
	pages = {6:1{\textendash}6:21 - 6:1{\textendash}6:21},
	abstract = {Researchers have invested much effort in constructing models of the state-dependent (sometimes risk-averse and sometimes risk-prone) nature of human decision making. An important open question is how state-dependent risk behavior can arise and remain prominent in populations. We believe that one part of the answer is the interplay between risk-taking and sequentiality of choices in populations subject to evolutionary population dynamics. To support this hypothesis, we provide simulation and analytical results for evolutionary lottery games, including results on evolutionary stability. We consider a parameterized class of imitation dynamics in which the parameter 0 <= α <= 1 yields the replicator dynamic with α = 1 and the imitate-the-better dynamic with α = 0. Our results demonstrate that for every population dynamic in this class except for the replicator dynamic, the interplay between risk-taking and sequentiality of choices allows state-dependent risk behavior to have an evolutionary advantage over expected-value maximization.},
	keywords = {decision theory, Evolutionary games, Population Dynamics, risk},
	isbn = {2157-6904},
	doi = {10.1145/1858948.1858954},
	url = {http://doi.acm.org/10.1145/1858948.1858954},
	author = {Roos,Patrick and Carr,J. Ryan and Nau, Dana S.}
}
@conference {18151,
	title = {A decision theoretic framework for analyzing binary hash-based content identification systems},
	booktitle = {Proceedings of the 8th ACM workshop on Digital rights management},
	series = {DRM {\textquoteright}08},
	year = {2008},
	month = {2008///},
	pages = {67 - 76},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Content identification has many applications, ranging from preventing illegal sharing of copyrighted content on video sharing websites, to automatic identification and tagging of content. Several content identification techniques based on watermarking or robust hashes have been proposed in the literature, but they have mostly been evaluated through experiments. This paper analyzes binary hash-based content identification schemes under a decision theoretic framework and presents a lower bound on the length of the hash required to correctly identify multimedia content that may have undergone modifications. A practical scheme for content identification is evaluated under the proposed framework. The results obtained through experiments agree very well with the performance suggested by the theoretical analysis.},
	keywords = {content fingerprinting, content identification, decision theory},
	isbn = {978-1-60558-290-0},
	doi = {10.1145/1456520.1456532},
	url = {http://doi.acm.org/10.1145/1456520.1456532},
	author = {Varna,Avinash L. and Swaminathan,Ashwin and M. Wu}
}
@conference {11896,
	title = {Kernel snakes: non-parametric active contour models},
	booktitle = {IEEE International Conference on Systems, Man and Cybernetics, 2003},
	volume = {1},
	year = {2003},
	month = {2003/10//},
	pages = {240- 244 vol.1 - 240- 244 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, a new non-parametric generalized formulation to statistical pressure snakes is presented. We discuss the shortcomings of the traditional pressure snakes. We then introduce a new generic pressure model that alleviates these shortcomings, based on the Bayesian decision theory. Non-parametric techniques are used to obtain the statistical models that drive the snake. We discuss the advantages of using the proposed non-parametric model compared to other parametric techniques. Multi-colored-target tracking is used to demonstrate the performance of the proposed approach. Experimental results show enhanced, real-time performance.},
	keywords = {Active contours, Artificial intelligence, Bayes methods, Bayesian decision theory, Bayesian methods, decision theory, Deformable models, Image edge detection, Image segmentation, Intelligent robots, Kernel, kernel snakes, Laboratories, multicolored target tracking, nonparametric active contour models, nonparametric generalized formulation, nonparametric model, nonparametric statistics, nonparametric techniques, real time performance, Robot vision systems, statistical pressure snakes, target tracking},
	isbn = {0-7803-7952-7},
	doi = {10.1109/ICSMC.2003.1243822},
	author = {Abd-Almageed, Wael and Smith,C.E. and Ramadan,S.}
}
@article {16323,
	title = {Learning from examples: generation and evaluation of decision trees for software resource analysis},
	journal = {IEEE Transactions on Software Engineering},
	volume = {14},
	year = {1988},
	month = {1988/12//},
	pages = {1743 - 1757},
	abstract = {A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for one problem domain, specifically, that of software resource data analysis. The purpose of the decision trees is to identify classes of objects (software modules) that had high development effort, i.e. in the uppermost quartile relative to past data. Sixteen software systems ranging from 3000 to 112000 source lines have been selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4700 objects, capture a multitude of information about the objects: development effort, faults, changes, design style, and implementation style. A total of 9600 decision trees are automatically generated and evaluated. The analysis focuses on the characterization and evaluation of decision tree accuracy, complexity, and composition. The decision trees correctly identified 79.3\% of the software modules that had high development effort or faults, on the average across all 9600 trees. The decision trees generated from the best parameter combinations correctly identified 88.4\% of the modules on the average. Visualization of the results is emphasized, and sample decision trees are included},
	keywords = {Analysis of variance, Artificial intelligence, Classification tree analysis, Data analysis, decision theory, Decision trees, Fault diagnosis, Information analysis, machine learning, metrics, NASA, production environment, software engineering, software modules, software resource analysis, Software systems, Termination of employment, trees (mathematics)},
	isbn = {0098-5589},
	doi = {10.1109/32.9061},
	author = {Selby,R. W and Porter, Adam}
}
