Sentence examples for model based classifier from inspiring English sources

Due to poor classification accuracy of the threshold based scheme, we subsequently implemented a personalized Bayesian model based classifier that leverages user's response pattern (e.g., time taken to answer a question and accuracy) to authenticate users.

A sparse model based classifier that we proposed before [ 12] used the pixel by pixel classification, overlooking structural information so that there are much more isolated spots in the results leading to the low accuracy of the classification.

In machine learning many methods like SVM, neural networks, fuzzy logic and gaussian mixture model based classifiers are popular out of which SVM is the most popular.

The optimization with linear and polynomial kernels for PSSM profile based classifier model resulted in ∼91% accuracy for both the kernels.

In this paper, we explore a prediction model for PPIs combining weighted sparse representation based classifier and a novel protein representation.

However, our proposed model obtains good performance similar to or even better than those obtained by the methods using ensemble classifier, such as Random Forest and ensemble of HKNN, by using the single weighted representation based classifier.

This variant classifier possesses the advantages of both the traditional sparse representation based classifier and the Nearest Neighbor classifier.

Integrating 9 histone markers profiles, the model based on SVM classifier identified 42 independent miRNA TSSs from total 298 miRNA genes, and 132 predicted miRNA TSSs were identified in the same position as the TSS of their upstream genes.

Analytical treatment of soft learning; performance of the classifier model on the problem of discriminating toxicity data; classifier model based on experimentally derived PRM promoter response function; effect of non-uniformity of m u distribution in "master population".

In this paper, we propose a method of building credit-scoring models using fuzzy rule based classifiers.

However, in general, the machine-learning model based on the Random Forest classifier showed the most robust performance.