… learning, regression models have a strong statistical basis. The logistic regression models
and … were validated by speech emotion recognition experiments conducted on a Malayalam …

… We used binary logistic regression in SPSS with step-wise forward selection. The stopping
… selection algorithms, we utilized binary logistic regression because it is standard and is …

… emotion recognition is still an unexplored problem. This paper introduced Logistic Regression
(LR) with Gaussian kernel and Laplacian prior for EEG-based emotion recognition. The …

… “contempt” while the predictive model developed failed in predicting the emotions “anger”
and “fear”. The final logistic regression algorithm output developed in this research is shown in …

… studied the L 1/2 penalty in sparse logistic regression for three-classification EEG emotion
… logistic regression with the L 1/2 penalty is an effective technique for emotion recognition in …

… In order to better fuse physiological signals, we combine multi-kernel learning with kernel
logistic regression and call it multi-kernel logistic regression (MKLR). In MKLR, the input vector …

… Logistic regression After extracting features via the sparse auto-encoder, we fed the features
into two logistic regression (LR) to … To train the logistic regression, we consider the following …

… Moreover, a sequential logistic regression showed that facial emotion recognition was
able to distinguish between healthy and PD participants after demographic and cognitive …

… In our next experiment when Logistic Regression (LR) is used on original data set then the
f1 score is 52 % and in case of class balanced data the f1 score is 38%. Figure 3 shows the …

… The emotion recognition module classifies the text into different … , Logistic Regression
classifier based supervised learning approach is proposed to classify text into different emotions …