The dynamic chain classifiers on the other hand determines the best label order at the prediction phase daSilva2014 The above mentioned classifier produces a set of randomly generated label sequences and then validates the chain classifiers
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Get PriceSubject of study We studied dynamic networks capable of performing calculations from input data Aim We studied whether deep Q learning can be used for the construction of dynamic computer vision networks Methods In modern dynamically configurable systems image analysis is typically performed using a policy gradient algorithm We propose a method for hybrid Q learning by an image
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Get PriceBinary classifiers are traditionally studied by propositional logic PL PL can only represent them as white boxes under the assumption that the underlying Boolean function is fully known Binary classifiers used in practical applications and trained by machine learning are however opaque They are usually described as black boxes In this paper we provide a product modal logic called PLC
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Get Priceself train data Training data labels for the dynamic classifiers self status status of a simulation True for live and false for dead self modelfile path to the model file self models a dictory of dynamic classifiers with the format of {step now model} self normalization dict a dictionary for data normalizations
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