Autonomous vehicle navigation using evolutionary reinforcement learning

Article Abstract:

An evolutionary learning model can be utilized to resolve the problem of autonomous vehicle navigation. The scheme, with the help of a learning classifier system, was proven to be more effective in utilizing the reinforcement learning aspect than other learning classifier strategies. It employs genetic learning classifier and reinforcement learning to create effective rules involving the steering behavior of the vehicle.

Operations Research, Algorithms, Navigation

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Bootstrap re-sampling for unbalanced data in supervised learning

Article Abstract:

Research developing a framework for assessing the influence which re-sampling can have on learning classification problems under supervision is presented. Particular attention is given to the use of the bootstrap technique in identifying re-sampling proportions.

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Mathematical programming based heuristics for improving LP-generated classifiers for the multiclass supervised classification problem

Article Abstract:

Mixed integer linear programming methods, which are used to examine computational dataset problems, are presented.

Belgium, Methods, Analysis, Linear programming, Computational complexity, Computational complexity (Machine theory)

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