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Estimating the actual proportion of data from the input data stream could allow to dynamically adapt ensembles to reflect operational conditions.
Recently, some techniques have been proposed to adapt the fusion function of an ensemble according to class imbalance of the input data stream.
During normal operation, the predistortion block operates on the input data stream in such a way that nonlinearties in the DAC core and open-loop output driver are cancelled.
In order to protect against address errors occurring at the input, a two-stage LUT implementation is used that removes errors occurring in the input data stream as well as protection of the state mapping itself.
Each row in the profile window keeps metadata extracted from a randomly chosen tuple t of the input data stream.
The outputs of the RSC encoder used here are the original input data stream (systematic output) and the parity output.
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Real binary input data streams are fed into a synthesis filter bank from wavelet packet theory.
Dynamic Artificial Neural Network (DANN) has time dependent structure which is defined by experience of processing input data streams.
A resolution IV design was used to identify the significant features from a list of ten possible candidates to represent the input data streams.
The system utilizes distributed processing nodes with checkpointing to process input data streams and reliably compute a variety of estimator updates for studying relationships between equity prices and the functions of corresponding option-related variables.
This leads to an adjustable transmission rate, and the input data streams share the entire network resource during the transmission.
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