This paper presents a comparative study of EEG-based multiclass motor imagery classifiers based on Kullback-Leiber regularised Riemann Mean and support vector machine, hybrid one versus classifier, linear discriminant analysis, convolutional neural network. The is felt to be inter- est those researchers working in the classification EEG signals. work presented this helps understand basics diffe...

Multiclass logistic regression (MLR) is a fundamental machine learning model to do multiclass classification. However, it is very challenging to perform MLR on large scale data where the feature dimension is high, the number of classes is large and the number of data samples is numerous. In this paper, we build a distributed framework to support large scale multiclass logistic regression. Using...

the prediction of lithology is necessary in all areas of petroleum engineering. this means that todesign a project in any branch of petroleum engineering, the lithology must be well known. supportvector machines (svm’s) use an analytical approach to classification based on statistical learningtheory, the principles of structural risk minimization, and empirical risk minimization. in thisresearc...

this paper aims to propose an effective paroxysmal atrial fibrillation (paf) predictor which is based on the analysis of the heart rate variability (hrv) signal. predicting the onset of paf, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize the risks for the patients. this method consists of four st...

This paper describes our system created to detect stance in online discussions. The goal is to identify whether the author of a comment is in favor of the given target or against. We created an extended corpus of Czech news comments and evaluated a support vector machines classifier, a maximum entropy classifier, and a convolutional neural network.

In this paper we prove that the reliability of the classifications of individual instances, provided by a classifier, results in information gain with respect to the accuracy of the classifier. We illustrate this result using our new approach to classification reliability called version space support vector machines.