TY - JOUR
AU - Pal, Aman
AB - In this paper, we have formulated a Laplacian Least Squares Twin Support Vector Machine called Lap-LST-KSVC for semi-supervised multi-category k-class classification problem. Similar to Least Squares Twin Support Vector Machine for multi-classification(LST-KSVC), Lap-LST-KSVC, evaluates all the training samples into “1-versus-1-versus-rest” classification paradigm, so as to generate ternary output {−1, 0, +1}. Experimental results prove the efficacy of the proposed method over other inline Laplacian Twin Support Vector Machine(Lap-TWSVM) in terms of classification accuracy and computational time.
TI - Multi-category laplacian least squares twin support vector machine
JF - Applied Intelligence
DO - 10.1007/s10489-016-0770-6
DA - 2016-03-23
UR - https://www.deepdyve.com/lp/springer-journals/multi-category-laplacian-least-squares-twin-support-vector-machine-TO8csYzTFu
SP - 458
EP - 474
VL - 45
IS - 2
DP - DeepDyve
ER -