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Availability of cheap power through alternative means such as energy exchanges and bilateral agreements is resulting in short-term load forecasting gaining importance among industries, residential complexes and corporate buildings. Short-term forecasting over an hour or a day requires non-linear predictive models. Machine learning algorithms such as neural networks are inherently non-linear and are...
Herein presents a novel neural decision forest by bootstrap aggregating randomized neural decision trees with the purpose of untangling signal data representations and performing heirarchical classification. The individual trees in a neural decision forest embed randomized neural networks (rNet) with 1-hidden layer acting as bottlenecks and hard gating functions. The rNets are trained using a multi-class...
Neural networks are a powerful classification technique that are capable of discovering complex relationships between inputs and outputs. This powerful classification ability has meant that neural networks have been widely applied to medical domains. However, neural networks suffer from the so-called blackbox problem: they predict accurately but offer no explanation of how the decision has been derived...
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