BETTER LEARNING THROUGH IMPROVED DISTRIBUTIONAL MODELING
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With the 21st century well underway, machine learning algorithms have advanced considerably in the ability to tackle difficult recognition problems. However, machine recognition is still rife with challenges, some of which are a direct result of advances made, for instance, as applications of machine recognition generalize, it is increasingly important for algorithms to “know” when they have have no basis to make a prediction. Similarly, algorithms must be able draw correlations across different training instances and be able to generalize from a training set how to make inferences about similarly ...