scholarly journals Vision Goes Symbolic Without Loss of Information Within the Preattentive Vision Phase: The Need to Shift the Learning Paradigm from Machine-Learning (from Examples) to Machine-Teaching (by Rules) at the First Stage of a Two-Stage Hybrid Remote... Part II

10.5772/27738 ◽  
2012 ◽  
Author(s):  
Andrea Baraldi
Keyword(s):  
Machine Learning ◽  
Learning From Examples ◽  
Learning Paradigm ◽  
Two Stage ◽  
Loss Of Information ◽  
Remote Part

scholarly journals Judgement of valence of musical sounds by hand and by heart, a machine learning paradigm for reading the heart

Heliyon ◽  
2021 ◽  
pp. e07565
Author(s):  
Ennio Idrobo-Ávila ◽  
Humberto Loaiza-Correa ◽  
Flavio Muñoz-Bolaños ◽  
Leon van Noorden ◽  
Rubiel Vargas-Cañas
Keyword(s):  
Machine Learning ◽  
Learning Paradigm ◽  
Musical Sounds

Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm

2017 ◽  
Vol 80 ◽  
pp. 77-96 ◽  
Author(s):  
Tadashi Araki ◽  
Pankaj K. Jain ◽  
Harman S. Suri ◽  
Narendra D. Londhe ◽  
Nobutaka Ikeda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Stroke Risk ◽  
Plaque Morphology ◽  
Learning Paradigm ◽  
Carotid Wall

A Two-Stage Machine Learning Classification Approach to Identify Extremism in Arabic Opinions

2021 ◽  
Vol 10 (2) ◽  
pp. 736-745
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Feature Selection Method ◽  
Support Vector ◽  
Two Stage ◽  
Machine Learning Classification ◽  
Second Stage ◽  
Testing Data ◽  
Stage Classification ◽  
Positive Dataset

The increased usage of the Internet and social networks allowed and enabled people to express their views, which have generated an increasing attention lately. Sentiment Analysis (SA) techniques are used to determine the polarity of information, either positive or negative, toward a given topic, including opinions. In this research, we have introduced a machine learning approach based on Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF) classifiers, to find and classify extreme opinions in Arabic reviews. To achieve this, a dataset of 1500 Arabic reviews was collected from Google Play Store. In addition, a two-stage Classification process was applied to classify the reviews. In the first stage, we built a binary classifier to sort out positive from negative reviews. In the second stage, however we applied a binary classification mechanism based on a set of proposed rules that distinguishes extreme positive from positive reviews, and extreme negative from negative reviews. Four major experiments were conducted with a total of 10 different sub experiments to fulfill the two-stage process using different X-validation schemas and Term Frequency-Inverse Document Frequency feature selection method. Obtained results have indicated that SVM was the best during the first stage classification with 30% testing data, and NB was the best with 20% testing data. The results of the second stage classification indicated that SVM has scored better results in identifying extreme positive reviews when dealing with the positive dataset with an overall accuracy of 68.7% and NB showed better accuracy results in identifying extreme negative reviews when dealing with the negative dataset, with an overall accuracy of 72.8%.

Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm

2017 ◽  
Vol 152 ◽  
pp. 23-34 ◽  
Author(s):  
Md. Maniruzzaman ◽  
Nishith Kumar ◽  
Md. Menhazul Abedin ◽  
Md. Shaykhul Islam ◽  
Harman S. Suri ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Learning Paradigm

A Mechanics-Based Similarity Measure for Text Classification in Machine Learning Paradigm

2020 ◽  
Vol 4 (2) ◽  
pp. 180-200 ◽  
Author(s):  
Venkatanareshbabu Kuppili ◽  
Mainak Biswas ◽  
Damodar Reddy Edla ◽  
K. J. Ravi Prasad ◽  
Jasjit S. Suri
Keyword(s):  
Machine Learning ◽  
Similarity Measure ◽  
Text Classification ◽  
Learning Paradigm
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