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Moreover, the existing methods need information regarding the process parameters, values of controller settings and are laborious.
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In a more recent work, Zheng et al. [43] proposed a weakly supervised penalty strategy for preimage learning in KPCA; however, their method needs information for both positive and negative classes.
The method needs information about the measurement error: in the case of the challenge a model relating the variance of the error to the expression level was provided by the organizers, in situations where this information is not available, it can be estimated from replicates [9].
This method needs information about the kernel function, which is usually not publicly available.
What is more, all active detecting methods need prior information, which is not available for the third party, and so they are not adapted to many practical applications.
Most current prediction methods need homology information, for example, sequence conservation from PSI-Blast, and require the existence of sequence or structure similar proteins.
Special attention has been paid to blind sensing methods that do not need information of the source signals and the propagation channels.
They also need information.
The method, which needs information obtained in a 2D segmentation stage, was tested on a volume containing, in total, 600 700 neurons belonging to three different cortical layers (15.4 MVoxels).
Unlike unsupervised methods, the class of supervised methods needs reference information.
These methods needed the information of a probability distribution and focused on providing estimators of local solutions.
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