Face recognition has been of interest to a growing number of researchers due to its applications on security. Within past years, there are numerous face recognition algorithms proposed by researchers. However, there is no unified framework for the integration. In this paper, we implement different existing well-known algorithms, Eigenface, Fisherface, Elastic Graph Matching (EGM), Support Vector Machine (SVM) and neural network, to give a comprehensive testing under same face databases. Moreover, we present a Face Recognition Committee Machine (FRCM), which is a novel approach for assembling the outputs of various face recognition algorithms to obtain a unified decision with improved accuracy. The machine consists of an ensemble of the above algorithms to cope with various face images. We have tested our system with ORL face database and Yale face database. A comparative experimental result of different algorithms with the committee machine demonstrates that the proposed system achieves improved accuracy over the individual algorithms.

Stock selection for hedge fund portfolios is a challenging problem that has previously been tackled by many machine-learning, genetic and evolutionary systems, including both Genetic Programming (GP) and Support Vector Machines (SVM). But which is the better? We provide a head-to-head evaluation of GP and SVM applied to this real-world problem, including both a standard comparison of returns on investment and a comparison of both techniques when extended with a “voting” mechanism designed to improve both returns and robustness to volatile markets. Robustness is an important additional dimension to this comparison, since the markets (the environment in which the GP or SVM solution must survive) are dynamic and unpredictable. Our investigation highlights a key difference in the two techniques, showing the superiority of the GP approach for this problem.

Face recognition has been of interest to a growing number of researchers due to its applications on security. Within past years, there are numerous face recognition algorithms proposed by researchers. However, there is no unified framework for the integration. In this paper, we implement different existing well-known algorithms, Eigenface, Fisherface, Elastic Graph Matching (EGM), Support Vector Machine (SVM) and neural network, to give a comprehensive testing under same face databases. Moreover, we present a Face Recognition Committee Machine (FRCM), which is a novel approach for assembling the outputs of various face recognition algorithms to obtain a unified decision with improved accuracy. The machine consists of an ensemble of the above algorithms to cope with various face images. We have tested our system with ORL face database and Yale face database. A comparative experimental result of different algorithms with the committee machine demonstrates that the proposed system achieves improved accuracy over the individual algorithms.

Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment in which the GP solution must survive) are dynamic, unpredictable and unforgiving. How can GP be improved so that solutions are produced that are robust to non-trivial changes in the environment? We explore an approach that uses a voting committee of GP individuals with differing phenotypic behaviour.