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Unlike the BoVW, our model is able to learn how to perform feature extraction, feature encoding, and classification guided by the classification error.
This distribution is most likely influenced by the classification error discussed previously; however, the genomewide distribution may serve as a baseline level for comparison with subsets of genes.
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The objective function in Eq. (5), which is utilized to quantify searched solutions, is defined by dividing the classification error by the number of eliminated features.
We pose the widely used bag-of-visual-words (BoVW) [5] method as a neural network, which allows it to refine the feature extraction and clustering functions by backpropagating the classification error.
Wrapper methods on the other hand perform evaluation by training the classification error using a validation set.
The best number of features and parameters (C and γ) were obtained by minimizing the classification error of SVM based on 10 iterations of 10-fold cross-validation.
Our preliminary formal analysis of FABS shows that the expected error and variance of the estimated scores by FABS will be within a manageable range given the classification error by the bipartition.
The spectrogram reconstruction error in audio source separation task is reduced by relatively 6.7% and the classification error in audio scene classification task is reduced by relatively 6.5%, when compared with the traditional frequency filter banks.
After completing the anonymization processes for each dataset, we first calculated the classification error by using RapidMiner Studio.
On the other hand, the secondary structure coming with a substantial level of overlap between classes is characterized by relatively higher values of the classification error.
Breiman proposed the "out-of-bag" method for estimating statistics of bagged classifiers, which was subsequently applied by other authors to estimate the classification error.
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