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Elements of information theory
Elements of information theory
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Machine Learning - Special issue on learning with probabilistic representations
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Learning in graphical models
Advanced inference in Bayesian networks
Learning in graphical models
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Bayesian Networks and Decision Graphs
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
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Model Averaging for Prediction with Discrete Bayesian Networks
The Journal of Machine Learning Research
Predicting football results using Bayesian nets and other machine learning techniques
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UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bayesian Model Averaging of Naive Bayes for Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Structure identification of Bayesian classifiers based on GMDH
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IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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The increased availability of information technologies has enabled law enforcement agencies to compile databases with detailed information about major felonies. Machine learning techniques can utilize these databases to produce decision-aid tools to support police investigations. This paper presents a methodology for obtaining a Bayesian network (BN) model of offender behavior from a database of cleared homicides. The BN can infer the characteristics of an unknown offender from the crime scene evidence, and help narrow the list of suspects in an unsolved homicide. Our research shows that 80% of offender characteristics are predicted correctly on average in new single-victim homicides, and when confidence levels are taken into account this accuracy increases to 95.6%.