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In [35], Wang and Wu dealt with variations in execution rate by combining an SVM-based classification algorithm with DTW.

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The proposed prediction framework named "ProteRNA" combines a SVM-based classifier with conserved residue discovery by WildSpan to identify the residues that interact with RNA in a RNA-binding protein.

In this way, we combined ModelEvaluator, a SVM-based model quality assessment program (MQAP) with a clustering-based MQAP.

Third, we extend our design by combining the SVM-based predictions with annotations generated using sequence alignment.

These are classified by a SVM-based approach.

A hybrid model was proposed by combining a support vector machine (SVM) model with an empirical equation for more accurate prediction of the polarization curves of a PEM (polymer electrolyte membrane) fuel cell under various operating conditions.

To combine information obtained from the local and global variations, an SVM-based fusion strategy at score level was performed.

This paper proposes an evolutionary approach to designing an SVM-based classifier (named ESVM) by simultaneous optimization of automatic feature selection and parameter tuning using an intelligent genetic algorithm, combined with k-fold cross-validation regarded as an estimator of generalization ability.

One of the first methods was POPI, which is an SVM-based predictor developed by Tung and Ho [ 74].

For performance comparison, we also developed an SVM-based speaker identification system by using the LIBSVM version 3.11 toolkit [19].

We employed an SVM-based approach to construct a classifier that can be used to predict the class membership as being MSMG or not-MSMG for genes by combining genome-wide mRNA expression profiling data and ChIP-seq data.

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