2,047 research outputs found
BDD, BNN, and FPGA on Fuzzy Techniques for Rapid System Analysis
This paper looks at techniques to simplify data analysis of large multivariate military sensor systems. The approach is illustrated using representative raw data from a video-scene analyzer. First, develop fuzzy neural net relations using Matlab. This represents the best fidelity fit to the data and will be used as reference for comparison. The data is then converted to Boolean, and using Boolean Decision Diagrams (BDD) techniques, to find similar relations between input vectors and output parameter. It will be shown that such Boolean techniques offer dramatic improvement in system analysis time, and with minor loss of fidelity. To further this study, Boolean Neural Net techniques (BNN) were employed to bridge the Fuzzy Neural Network (FNN) to BDD representations of the data. Neural network approaches give an estimation method for the complexity of Boolean Decision Diagrams, and this can be used to predict the complexity of digital circuits. The neural network model can be used for complexity estimation over a set of BDDs derived from Boolean logic expressions. Experimental results show good correlation with theoretical results and give insights to the complexity. The BNN representations can be useful as a means to FPGA implementation of the system relationships and can be used in embedded processor based multi-variate situations
Improved Reinforcement Learning with Curriculum
Humans tend to learn complex abstract concepts faster if examples are
presented in a structured manner. For instance, when learning how to play a
board game, usually one of the first concepts learned is how the game ends,
i.e. the actions that lead to a terminal state (win, lose or draw). The
advantage of learning end-games first is that once the actions which lead to a
terminal state are understood, it becomes possible to incrementally learn the
consequences of actions that are further away from a terminal state - we call
this an end-game-first curriculum. Currently the state-of-the-art machine
learning player for general board games, AlphaZero by Google DeepMind, does not
employ a structured training curriculum; instead learning from the entire game
at all times. By employing an end-game-first training curriculum to train an
AlphaZero inspired player, we empirically show that the rate of learning of an
artificial player can be improved during the early stages of training when
compared to a player not using a training curriculum.Comment: Draft prior to submission to IEEE Trans on Games. Changed paper
slightl
Fairness in Credit Scoring: Assessment, Implementation and Profit Implications
The rise of algorithmic decision-making has spawned much research on fair
machine learning (ML). Financial institutions use ML for building risk
scorecards that support a range of credit-related decisions. Yet, the
literature on fair ML in credit scoring is scarce. The paper makes two
contributions. First, we provide a systematic overview of algorithmic options
for incorporating fairness goals in the ML model development pipeline. In this
scope, we also consolidate the space of statistical fairness criteria and
examine their adequacy for credit scoring. Second, we perform an empirical
study of different fairness processors in a profit-oriented credit scoring
setup using seven real-world data sets. The empirical results substantiate the
evaluation of fairness measures, identify more and less suitable options to
implement fair credit scoring, and clarify the profit-fairness trade-off in
lending decisions. Specifically, we find that multiple fairness criteria can be
approximately satisfied at once and identify separation as a proper criterion
for measuring the fairness of a scorecard. We also find fair in-processors to
deliver a good balance between profit and fairness. More generally, we show
that algorithmic discrimination can be reduced to a reasonable level at a
relatively low cost.Comment: Preprint submitted to European Journal of Operational Researc
BDD, BNN, and FPGA on Fuzzy Techniques for Rapid System Analysis
This paper looks at techniques to simplify data analysis of large multivariate military sensor systems. The approach is illustrated using representative raw data from a video-scene analyzer. First, develop fuzzy neural net relations using Matlab. This represents the best fidelity fit to the data and will be used as reference for comparison. The data is then converted to Boolean, and using Boolean Decision Diagrams (BDD) techniques, to find similar relations between input vectors and output parameter. It will be shown that such Boolean techniques offer dramatic improvement in system analysis time, and with minor loss of fidelity. To further this study, Boolean Neural Net techniques (BNN) were employed to bridge the Fuzzy Neural Network (FNN) to BDD representations of the data. Neural network approaches give an estimation method for the complexity of Boolean Decision Diagrams, and this can be used to predict the complexity of digital circuits. The neural network model can be used for complexity estimation over a set of BDDs derived from Boolean logic expressions. Experimental results show good correlation with theoretical results and give insights to the complexity. The BNN representations can be useful as a means to FPGA implementation of the system relationships and can be used in embedded processor based multi-variate situations
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