Paulo Fernandes - Academia.edu (original) (raw)

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Papers by Paulo Fernandes

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ABSTRACT This paper describes a method to obtain symbolic solution of large stochastic models usi... more ABSTRACT This paper describes a method to obtain symbolic solution of large stochastic models using Gauss-Jordan elimination. Such solution is an efficient alternative to standard simulations and it allows fast and exact solution of very large and complex models that are hard to be dealt even with iterative numerical methods. The proposed method assumes the system described as a structured (modular) Markovian system with discrete states for each system module and transitions among those states ruled by Markovian processes. The mathematical representation of such system is made by a Kronecker (Tensor) formula, i.e., a tensor formulation of small matrices representing each system module transitions and occasional dependencies among modules. Preliminary results of the proposed solution indicate the expected efficiency of the proposed solution.

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ABSTRACT Some top data mining algorithms, as ensemble classifiers, may be inefficient to very lar... more ABSTRACT Some top data mining algorithms, as ensemble classifiers, may be inefficient to very large data set. This paper makes an initial proposal of a distributed ensemble classifier algorithm based on the popular Random Forests for Big Data. The proposed algorithm aims to improve the efficiency of the algorithm by a distributed processing model called MapReduce. At the same time, our proposed algorithm aims to reduce the randomness impact by following an algorithm called Stochastic Aware Random Forests - SARF.

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