Fuzzy Twin Support Vector Machines for Pattern Classification

Detecting Happiness in Human Face using Unsupervised Twin-Support Vector Machines

“…The Q-function of M-step (10) is formed by using the linear shape model (6) and global objective maximizing of PDM landmark (4) with the Expectation-Maximization (EM) algorithm [31] (10).…”

“…In that we survey [31][32][33] Twin Support Vector Machines is better performance II. RELATED WORK In this paper, the real-time facial expression recognition system is proposed for detecting the emotion in human from face.…”

“… Second, the Twin Support Vector Machines (TWSVMs) [31][32][33] is formulated for classification of facial expression with help of Facial Animation Parameters (FAPs) [17] and [19] is extracted the features points are formed the minimal feature vector displacements.…”

Multiclass Least Squares Twin Support Vector Machine for Pattern Classification

“…In this approach, the patterns of each class lie in the close proximity of one hyper-plane and maintain clear separation with other. On the basis of SVM and GEPSVM, Jayadeva et al proposed a novel binary classifier, Twin Support Vector Machine (TWSVM), which classifies the patterns of two classes by using two non-parallel hyper-planes [22]. TWSVM solves a pair of QPPs instead of single complex QPP as in SVM which makes the learning of TWSVM four times faster as compared to conventional SVM [22][23].…”

“…On the basis of SVM and GEPSVM, Jayadeva et al proposed a novel binary classifier, Twin Support Vector Machine (TWSVM), which classifies the patterns of two classes by using two non-parallel hyper-planes [22]. TWSVM solves a pair of QPPs instead of single complex QPP as in SVM which makes the learning of TWSVM four times faster as compared to conventional SVM [22][23]. In SVM, all patterns together provide constraints to QPP, while in TWSVM patterns of one of the two classes provide constraints to each QPP.…”