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One of the necessary constraint sets is the smoothness constraint set (SCS) computed in the spatial domain, which also requires fully decoding all the compressed input copies, not to mention the computational load required for the computation of the smoothness criteria.
Compressed input and output are supported using either gzip or bzip2 formats.
We note that reading compressed input is nowadays a convenient feature of many tools (e.g., de novo assemblers Velvet [ 91] and ABySS [ 92]), but not always it brings improvements in speed.
In an initial learning step the system is trained on the compressed input data so as to classify different situations and to associate appropriate behaviours to these situations.
Thus, the complexity of the proposed method is approximately equal to the complexity required to decode all available compressed input copies.
At run time the compressed input data are fed into different B-spline fuzzy controllers which determine the correspondence between the actual situation and the situation they were trained for.
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We performed some benchmark tests about the Snazer's ability to compress input data.
PLSR is efficient for compressing inputs, intermediate states and output variables into their most relevant subspaces (spanned by the estimated latent variables, also called PLS components (PCs)), and hence provides a versatile means for data compression by reducing the rank of both regressors (X) and responses (Y).
The compression method described in [80] is one of this kind of solutions, it first clusters the input data and then compresses these input data via the clustering results while the study [81] also used clustering method to improve the performance of the compression process.
GS compresses the input PPI graph into a summary graph which shows a high-level structure of the input graph.
This half-second delay seems to be the time required for processing and compressing sensory input.
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