<p>Each horizontal row shows Precision, Recall and F-score performance for a class using alternative methods. <i>Unique Rate</i> shows the percentage of unique entity mentions seen in the cross-validation test for each class. <i>ALL</i> shows micro-averaged F-score.</p
<p>D: dimensionality; AUC: area under ROC curve; Spe: specificity; Pre: precision; Sen: sensitivity;...
<p>There are 10 independent runs on the training (LFP-TRN) and test (LFP-TEST) data sets.</p
Appropriate training data always play an important role in constructing an efficient classifier to s...
<p>Each horizontal row shows a combination of features and the associated F-scores for each class on...
<p>The sensitivity, specificity and accuracy of each of three classifiers (Linear SVM, RBF SVM, NN) ...
This paper explores the scalability issues associated with solving the Named Entity Recognition (NER...
This paper investigates stacking and voting methods for combining strong classifiers like boosting...
<p>Comparison of predictive performances of single feature classes. All values are taken from SVM pr...
This paper investigates stacking and voting methods for combining strong classifiers like boosting, ...
a<p>The overall recognition accuracy is 97.40±0.95%.</p>b<p>The class precision is the percentage of...
LDA feature selection (a) selects 192 features with most being relative PSD band features and standa...
Machine Learning is described in today’s Information Technology world as one of the most promising r...
<p>The table shows the cross-validation performance of our method on the labeled data points. The me...
<p>(Left) EF (Right) Ratio of true positive rate to the false positive rate, at 0.5%, 1.0%, 2.0%, an...
(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...
<p>D: dimensionality; AUC: area under ROC curve; Spe: specificity; Pre: precision; Sen: sensitivity;...
<p>There are 10 independent runs on the training (LFP-TRN) and test (LFP-TEST) data sets.</p
Appropriate training data always play an important role in constructing an efficient classifier to s...
<p>Each horizontal row shows a combination of features and the associated F-scores for each class on...
<p>The sensitivity, specificity and accuracy of each of three classifiers (Linear SVM, RBF SVM, NN) ...
This paper explores the scalability issues associated with solving the Named Entity Recognition (NER...
This paper investigates stacking and voting methods for combining strong classifiers like boosting...
<p>Comparison of predictive performances of single feature classes. All values are taken from SVM pr...
This paper investigates stacking and voting methods for combining strong classifiers like boosting, ...
a<p>The overall recognition accuracy is 97.40±0.95%.</p>b<p>The class precision is the percentage of...
LDA feature selection (a) selects 192 features with most being relative PSD band features and standa...
Machine Learning is described in today’s Information Technology world as one of the most promising r...
<p>The table shows the cross-validation performance of our method on the labeled data points. The me...
<p>(Left) EF (Right) Ratio of true positive rate to the false positive rate, at 0.5%, 1.0%, 2.0%, an...
(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...
<p>D: dimensionality; AUC: area under ROC curve; Spe: specificity; Pre: precision; Sen: sensitivity;...
<p>There are 10 independent runs on the training (LFP-TRN) and test (LFP-TEST) data sets.</p
Appropriate training data always play an important role in constructing an efficient classifier to s...