Support vector machine soft margin classifiers are important learning algorithms for classification problems. They can be stated as convex optimization problems and are suitable for a large data setting. Linear programming SVM classifier is specially efficient for very large size samples. But little is known about its convergence, compared with the well understood quadratic programming SVM clas...

Support Vector Machines (SVMs) for classification tasks produce sparse models by maximizing the margin. Two limitations of this technique are considered in this work: firstly, the number of support vectors can be large and, secondly, the model requires the use of (Mercer) kernel functions. Recently, some works have proposed to maximize the margin while controlling the sparsity. These works also...

This paper describes a soft-error tolerant and marginenhanced nMOS-pMOS reversed 6T SRAM cell. The 6T SRAM bitcell comprises pMOS access and driver transistors, and nMOS load transistors. Therefore, the nMOS and pMOS masks are reversed in comparison with those of a conventional bitcell. In scaled process technology, The pMOS transistors present advantages of small random dopant fluctuation, str...

We propose a new discriminative learning framework, called soft margin estimation (SME), for estimating parameters of continuous density hidden Markov models. The proposed method makes direct usage of the successful ideas of soft margin in support vector machines to improve generalization capability, and of decision feedback learning in minimum classification error training to enhance model sep...

In this paper, we show that the popular C-SVM, soft-margin support vector classifier is equivalent to minimization of Buffered Probability of Exceedance (bPOE) by introducing a new SVM formulation, called the EC-SVM, which is derived as a bPOE minimization problem. Since it is derived from a simple bPOE minimization problem, the EC-SVM is simple to interpret with a meaningful free parameter, op...

In this paper, we derive a general formulation of support vector machines for classification and regression respectively. Le loss function is proposed as a patch of L1 and L2 soft margin loss functions for classifier, while soft insensitive loss function is introduced as the generalization of popular loss functions for regression. The introduction of the two loss functions results in a general ...

We describe two patients aged 16 and 25 years with osteogenesis imperfecta who sustained displaced fractures of the acetabulum following minor trauma. The femoral heads were deformed by impact against the acetabular margin and both cases underwent surgical reconstruction. The quality of the bone and soft tissues made the operations challenging. There were potential complications specific to ost...

We propose a “soft greedy” learning algorithm for building small conjunctions of simple threshold functions, called rays, defined on single real-valued attributes. We also propose a PAC-Bayes risk bound which is minimized for classifiers achieving a non-trivial tradeoff between sparsity (the number of rays used) and the magnitude of the separating margin of each ray. Finally, we test the soft g...

Vapnik’s result that the expectation of the generalisation error of the optimal hyperplane is bounded by the expectation of the ratio of the number of support vectors to the number of training examples is extended to a broad class of kernel machines. The class includes Support Vector Machines for soft margin classification and regression, and Regularization Networks with a variety of kernels an...

AbstractObjectives There remains no consensus on what constitutes an adequate margin of resection for non-infiltrative soft-tissue sarcomas (STSs). We aimed to investigate the role margins in millimetres STSs. Methods 502 patients who underwent surgical a localized, non-infiltrative, high-grade STSs were studied. The prognostic significance width was analysed and comp...