The tremendous recent success of deep neural networks (DNNs) has sparked a surge interest in understanding their predictive ability. Unlike the human visual system which is able to generalize robustly and learn with little supervision, DNNs normally require massive amount data new concepts. In addition, research works also show that are vulnerable adversarial examples-maliciously generated imag...

recently, tuning the weights of the rules in fuzzy rule-base classification systems is researched in order to improve the accuracy of classification. in this paper, a margin-based optimization model, inspired by support vector machine classifiers, is proposed to compute these fuzzy rule weights. this approach not only  considers both accuracy and generalization criteria in a single objective fu...

In this paper the problem of restricted complexity stability margin maximization (RCSMM) for single-input singleoutput (SISO) plants affected by rank one real perturbations is considered. This problem amounts to maximizing the real l2 parametric stability margin over an assigned class of restricted complexity controllers, which are described by rational transfer functions of fixed order with co...

An efficient supervised orthogonal nonlinear dimensionality reduction algorithm, namely orthogonal margin maximization projection (OMMP), is presented for gait recognition in this paper. Taking the local neighborhood geometry structure and class information into account, the proposed algorithm aims to find a projecting matrix by maximizing the local neighborhood margin between the different cla...

In a classification problem, hard margin SVMs tend to minimize the generalization error by maximizing the margin. Regularization is obtained with soft margin SVMs which improve performances by relaxing the constraints on the margin maximization. This article shows that comparable performances can be obtained in the linearly separable case with the Ho–Kashyap learning rule associated to early st...

In a classification problem, hard margin SVMs tend to minimize the generalization error by maximizing the margin. Regularization is obtained with soft margin SVMs which improve performances by relaxing the constraints on the margin maximization. This article shows that comparable performances can be obtained in the linearly separable case with the Ho–Kashyap learning rule associated to early st...

In this paper, the combined sensitivity and gain margin problem for SISO linear systems is formulated and solved using a complex function interpolation technique. It is proved that this problem always has a real rational solution provided it is solvable in the complex irrational sense. The sensitivity minimization problem subject to a gain margin constraint and its dual problem are also conside...

Margin maximizing properties play an important role in the analysis of classi£cation models, such as boosting and support vector machines. Margin maximization is theoretically interesting because it facilitates generalization error analysis, and practically interesting because it presents a clear geometric interpretation of the models being built. We formulate and prove a suf£cient condition fo...