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We propose to optimize the assignment edge weights for optimal VS performance on a data set of known ligands and decoys, which is usually available even in the early stages of drug discovery.
In Section 4, we propose to optimize the network sum rate through a diagonal matrix W. The precoding vector optimization within the IA subspaces is presented in Section 5.
Secondly, we propose to optimize jointly the set of precoder bases within their associated precoding subspaces.
Secondly, we propose to optimize the precoding vectors at each transmitter within its precoding subspace.
In this work, we propose to optimize the ROC performance directly according to the fusion classifier design.
As a result, we propose to optimize the prediction filter p j ( H H ) by minimizing the global prediction error, as described in detail in the next section.
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Since the output of a prediction filter may be used as an input for other prediction filters, we then propose to optimize such a filter by minimizing a weighted ℓ1 criterion related to the global prediction error.
Since the output of a prediction filter may be used as an input for the other prediction filters, we then propose to optimize such a filter by minimizing a weighted ℓ1 criterion related to the global rate-distortion performance.
In the previous subsection, we have proposed to optimize the precoding subspaces using iterative processing when an MMSE is applied at the receiver.
Next, a multi-dimensional optimization algorithm is proposed to optimize the objectives of a mobile user.
A hybrid protocol is proposed to optimize this tradeoff.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com