The Interactive Biometric Platform


The Interactive Biometric Platform – To obtain an informed opinion on the proposed method, the authors have designed two software projects: The Inter-Agency Biometric Machine Learning, which implements the new algorithms and is based on a prototype. The Machine Learning project focuses on the identification of the object, the data collection and the user experience. The main contributions of the Machine Learning project involved: (1) developing an online algorithm for solving the object recognition problem, (2) building an end-to-end solution for the application, (3) developing the algorithm and making use of the generated samples.

The Bayesian network has the opportunity of having a fundamental role in many problems in finance. With the interest of finance, there has been a large effort in applying Bayesian networks in the financial sector. In this paper, we describe a new algorithm for Bayesian networks, called Pareto-Bayesian networks (PBN). The method is presented as a special case of a general class of PBNs. Based on the previous work, we show that the PBN can be efficiently trained as a Markov decision process or an ensemble of networks. A first example is given. It is shown that if two PBNs are combined together, they can be successfully identified from a database using a novel algorithm. The results of our experiments on real financial applications show that the PBN can be the primary tool in solving practical risk-theoretic decision-making task.

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The Interactive Biometric Platform

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    Mixture of DAG-causal patterns and conditional probability in trainable Bayesian networksThe Bayesian network has the opportunity of having a fundamental role in many problems in finance. With the interest of finance, there has been a large effort in applying Bayesian networks in the financial sector. In this paper, we describe a new algorithm for Bayesian networks, called Pareto-Bayesian networks (PBN). The method is presented as a special case of a general class of PBNs. Based on the previous work, we show that the PBN can be efficiently trained as a Markov decision process or an ensemble of networks. A first example is given. It is shown that if two PBNs are combined together, they can be successfully identified from a database using a novel algorithm. The results of our experiments on real financial applications show that the PBN can be the primary tool in solving practical risk-theoretic decision-making task.


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