Abstmet-Any two design performance indices used in wntral system design have the potential to conflict with each other and a good control system is oiten some kind of compromise which optimizes neither index but secures satisfactory values for bath. The objective of this paper is to study the two indices of sensitivity and phase margin simultaueously and reveal how the indices affect each other...

In this work, a semi-obnoxious facility must be located in the Euclidean plane to give service to a group of customers. Simultaneously, a set of populated areas, with shapes approximated via polygons, must be protected from the negative effects derived from that facility. The problem is formulated as a margin maximization model, following a strategy successfully used in Support Vector Machines....

This paper presents a convex optimization method for the feedback-loop tradeoff of L1 adaptive controller. Both problems of performance improvement and time-delay margin maximization are shown to be cast into Linear Matrix Inequality (LMI) type conditions. First, each of these conditions is studied separately towards a distinct objective, and next two similar LMI algorithms are proposed for opt...

In this paper, we present an optimal, computationally efficient, integer-bit power allocation algorithm for discrete multitone modulation. Using efficient lookup table searches and a Lagrange-multiplier bisection search, our algorithm converges faster to the optimal solution than existing techniques and can replace the use of suboptimal methods because of its low computational complexity. Fast ...

In this paper, we compare two powerful kernel-based learning machines, support vector machines (SVM) and relevance vector machines (RVM), within the framework of hidden Markov model-based speech recognit ion. Both machines provide nonlinear discriminative classification ability: the SVM by kernelbased margin maximization and the RVM using a Bayesian probabilistic framework. The hybrid systems a...

In ranking problems, the goal is to learn a ranking function r(x) ∈ R from labeled pairs x, x′ of input points. In this paper, we consider the related comparison problem, where the label y ∈ {−1, 0, 1} indicates which element of the pair is better, or if there is no significant difference. We cast the learning problem as a margin maximization, and show that it can be solved by converting it to ...

Incorporating feature selection in nonlinear SVMs leads to a large and challenging nonconvex minimization problem, which can be prone to suboptimal solutions. We use a second order optimization method that utilizes eigenvalue information and is less likely to get stuck at suboptimal solutions. We devise an alternating optimization approach to tackle the problem efficiently, breaking it down int...

The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignores the minimization of the radius. Several approaches have been proposed to integrate radius and margin for joint learning of feature transformation and SVM classifier. However, most of them either require the form of...

Face recognition has made extraordinary progress owing to the advancement of deep convolutional neural networks (CNNs). The central task of face recognition, including face verification and identification, involves face feature discrimination. However, the traditional softmax loss of deep CNNs usually lacks the power of discrimination. To address this problem, recently several loss functions su...