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Clearly, the proposed adaptive system with both BER threshold approaches performs better than the non-adaptive system.
But in the high noise condition with SNR = -5 dB, the MI based approaches performs distinctly better than the entropy based ones.
While training the ENCAPP classifier, it is necessary to check which of the two approaches performs better.
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The phenomenon was previously described by experimental approaches performed in the laboratory.
All transfer learning approaches perform better than the baseline approaches.
However, both approaches performed equally well in cross-validation.
Most of approaches perform a k-means [19] on descriptors.
All the transfer learning approaches performed better than the baseline approach.
CF based approaches perform better with the availability of more data.
Second, most current MI-based approaches perform feature selection sequentially starting from high-ranked features.
Indeed, the fingerprint approaches perform quite well for ER, mineralcorticoid receptor (MR) and RXR.
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