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It is also possible that a sampling of features from a large class of generalized features within the training phase of the algorithm [ 44], will help improve results.
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Liu et al. [25] proposed an optimal sampling of Gabor features using PCA for face recognition.
Our findings, therefore, challenge the proposal that neural lateralization for speech processing is due solely to asymmetric temporal sampling of acoustic features (Poeppel 2003).
If participants were able to select the correct action in response to rotated versions of the original scenes this would suggest that behavioral performance was based on view-independent scene representations, rather than the sampling of individual features.
A RF classifier is composed of a large number of decision trees, each trained using a random subset of samples (bagging) and constructed by selecting the best splitting features from random and independent sampled subsets of features [ 20, 21].
Different values for the number of training samples, number of features, and the length of the timeframes are used when learning the models.
Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space.
To handle the problem of small ratio of samples to features, in this paper, we have also utilized three well-known multivariate feature selection methods viz.
Further, in case of mental task classification, the ratio of samples to features is very small.
Table 1 Data features and diversity Dataset Number of features Sample Training sample Test sample 1 15 325 213 123 2 14 287 145 98 3 12 234 89 45.
The sample of H3-80 feathees the lowest peak intensity as the highest acid concentration and temperature among these samples.
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