Sentence examples for many other classifiers from inspiring English sources

SVM also have strong statistical theoretical foundations that many other classifiers do not possess and whose model is the global optimum.

This approach was not only straightforward but also successful, because naïve Bayes classifiers identify the parameters required for accurate classification using less training data than many other classifiers.

As Liaw and Wiener indicate [ 61] this strategy performs very well compared to many other classifiers, including discriminant analysis, logistic regression, support vector machines and neural networks [ 60].

The parameters (gene weights) in formula (1) are estimated independent of each other, making SEP more robust than many other classifiers such as Linear Discriminate Analysis, and robustness is essential for analysis performed on independent datasets.

For example, on accuracy aspect, random forest turns out to perform very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks [ 23], and is robust against over-fitting [ 22].

This technique performs very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks [ 21], provided that predictor variables are similar in their scale of measurement or number of categories [ 22].

This is possible since unlike many other sequence classifiers and k-mer counters [16,17,33], Strand uses no special encoding of sequence data and supports any Unicode character within the sequences.

However, there are many other types of classifiers that are currently being investigated.

Learning and classification of pixels was done by a support vector machine, although many other machine learning classifiers may be employed alternatively.

Now, many sequence-based methods adopted various predicted results from other classifiers, such as predicted secondary structure, predicted solvent accessibility and predicted disorder probabilities, to combine with position-specific scoring matrix (PSSM) as input for binding sites prediction.

The application of the belief network to other classifiers depends on the choice of classifier, but many classifiers have a natural way to use prior probabilities either directly or in the form of weights.