The classification, as the second step of the methodology, is implemented by means of an artificial neural network (ANN) trained with the back-propagation algorithm under "leave-one-out cross-validation". The ANN is a multi-layer perceptron, the architecture of which, is selected after a ...
In the present paper, a new methodological approach, for the classification of first episode schizophrenic patients (FES) against normal controls, is proposed. The first step of the methodology applied is the feature extraction, which is based on the combination of the multivariate autoregressive mode...
Alternatively, you can optimize a classifier by using theOptimizeHyperparametersname-value argument. For an example, seeOptimize Classifier Fit Using Bayesian Optimization. Generate Data The classification works on locations of points from a Gaussian mixture model. InThe Elements of Statistical Learning, ...
In the research of machine learning algorithms for classification tasks, the comparison of the performances of algorithms is extremely important, and a statistical test of significance for generalization error is often used to perform it in the machine learning literature. In view of the randomness of...
Find more on Classification Learner App in Help Center and File Exchange Tags predict crossvalidated svm cross-validated Products MATLAB Release R2020b Community Treasure Hunt Find the treasures in MATLAB Central and discover how the c...
Further, the classification of GDQS was based on the classification suggested by Bromage et al. as follow: lowest GDQS (scores lower than 15), medium GDQS (GDQS ranging between 15 and 23), and the score of 23 or more (≥ 23 GDQS) was considered as high GDQS [37]. Dietary patterns...
The model selected based on the CV-AUC criterion tends to have a larger predictive AUC and smaller classification error than those with tuning parameters selected using the AIC, BIC or EBIC. We illustrate the application of the -logistic regression with the CV-AUC criterion on three microarray ...
Detected peaks used to generate cross-validated classification models between different groups.Jing WangXinying WangShiyong LinChudi ChenCongrong WangQunying MaBo Jiang
The support vector machine (SVM) is a widely used tool for classification. Many efficient implementations exist for fitting a two-class SVM model. The user... T Hastie,S Rosset,R Tibshirani,... - 《Journal of Machine Learning Research》 被引量: 1108发表: 2004年 Adjustment of an Inverse ...
Examples include selecting the best classification tree using cross-validated classification error (Breiman et al., 1984) and variable selection in linear regression using cross-validated predictive squared error (Hjort, 1995).Padhraic SmythSixth international workshop on artificial intelligence and ...