scholarly journals A Real-Time System for Facial Expression Recognition using Support Vector Machines and k-Nearest Neighbor Classifier

2017 ◽  
Vol 159 (8) ◽  
pp. 23-29 ◽  
Author(s):  
Hend Ab. ◽  
A. A. ◽  
Elsaeed E.
Keyword(s):  
Facial Expression Recognition ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Expression Recognition ◽  
K Nearest Neighbor ◽  
Time System ◽  
Real Time System ◽  
Nearest Neighbor Classifier ◽  
Vector Machines ◽  
Neighbor Classifier

scholarly journals Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier

2018 ◽  
Vol 6 (4) ◽  
pp. 129-134 ◽  
Author(s):  
Jumoke Falilat Ajao ◽  
David Olufemi Olawuyi ◽  
Odetunji Ode Odejobi
Keyword(s):  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Chain Code ◽  
Nearest Neighbor Classifier ◽  
Digitized Images ◽  
Neighbor Classifier

This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.

scholarly journals Various Classifiers Performance Based Machine Learning Methods

2019 ◽  
Vol 8 (3S3) ◽  
pp. 305-310 ◽  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Research Work ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Vector Machines ◽  
Machine Learning Approach ◽  
Future Work ◽  
Neighbor Classifier

Classification is a form of data mining (regarding machine learning) approach that is helpful in the prediction of group membership for data instances, where the data input is used by the computer program for learning and thereafter this learning is used for classifying the fresh observation made. This data set might just be bi-class or it can be multi-class also. Few instances of the problems in classification include: speech identification, handwriting identification, bio metric detection, document classification etc. Many classification methods exist, which can be utilized for classification. In this research work, the fundamental classification approaches and few important kinds of classification approaches that include decision tree induction, Bayesian networks,k-nearest neighbor classifier and Support Vector Machines (SVM) and fuzzy learning classifiers with their merits, drawbacks, probable applications and challenges faced with the solution available. There are different problems that have an effect on the classification and prediction. The objective of this research work is to render an extensive review of various classification approaches in machine learning. At last, the future work intended on the best classification techniques for the input data are discussed.

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