Sentence examples for many standard classifiers from inspiring English sources

TSPs have two main advantages over many standard classifiers used in gene expression studies: (i) a TSP is based on only two genes, which leads to easily interpretable and inexpensive diagnostic tests and (ii) TSP classifiers are based on gene rankings, so they are more robust to variation in technical factors or normalization than classifiers based on expression levels of individual genes.

Standard classifiers based on Gaussian Mixture Models, Hidden Markov Models and Multilayer Perceptron are tested.

The performance is compared to standard classifiers from statistical pattern recognition.

The proposed algorithm, MOSC (Multi-Objective Sequence Classifier), is compared to standard classifiers and previous works on these real data sets and exhibits noticeably better classification performance.

We are comparing popular and easily accessible standard classifiers with a neural network paradigm.

The classifier, pronounced gè or ge in Mandarin, apart from being the standard classifier for many nouns, also serves as a general classifier, which may often (but not always) be used in place of other classifiers; in informal and spoken language, native speakers tend to use this classifier far more than any other, even though they know which classifier is "correct" when asked.

Most of the current literature does not define how many instances would be considered "limited", but they assume it to be too few to create an effective standard classifier.

Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles.

AUC and ROC are the measure of the standard classifier model which is good or bad.

We employ both terms here to be consistent with the standard classifier nomenclature.

The sumdiff/mul/sign-PAM classifier outperforms the standard PAM classifier in many datasets.