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The performance of Patterns by Deidre Cooper and Tilbury is wonderfully dedicated, while Piano, Violin, Viola, Cello seems just as compelling on disc as it was live in Huddersfield.
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The objective of this article was to improve the performance of pattern recognition systems when there is an overlapping feature vectors due to non-stationarity of the signals or the commonality that exist among different classes.
This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI.
Since higher dimensional feature vectors contain several irrelevant features that reduce the performance of pattern recognition methods, feature selection by itself can be used in most of the modern data classification methods to combat the issues resulting from the curse of high dimensionality [18, 19].
Previous studies have assessed the performance of pattern decoders in estimating sound location from the responses of ensembles of cortical neurons.
An indexed local cooling load (LCL) is adopted to evaluate the performance of airflow patterns.
Now let us compare the performance of both patterns P1 and P3 when using the proposed soft demapping algorithm.
Experiments were conducted using further simulations and data were collected in order to evaluate the performance of each design pattern.
Figure 2B also shows the performance of the pattern matching decoder.
Natural variability that occurs in high dimensional data has significant impact on lowering the performance of all pattern recognition methods.
The results indicate the developed Apta-LoopEnc is able to significantly improve the performance of different pattern recognition models.
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