In CDMA cellular networks, the soft handoff technique allows a mobile host to communicate with multiple base stations simultaneously, improving the transmission quality of the wireless channel and avoiding disconnection upon to base station switching. In this paper, we evaluate the influence of soft handoff on TCP (Transport Control Protocol) throughput in CDMA cellular networks through simulat...

A correct understanding of the prognostic effect of the surgical margin is essential in extremity soft tissue sarcoma (STS). If the status of surgical margin has by itself a significant impact on survival, wider surgical margins would be needed, and larger functional sequelae justified. However, if the status of surgical margin does not affect survival, closer surgical margins with a lower loss...

We show that support vector machines of the 1-norm soft margin type are universally consistent provided that the regularization parameter is chosen in a distinct manner and the kernel belongs to a specific class}the so-called universal kernels}which has recently been considered by the author. In particular it is shown that the 1-norm soft margin classifier with Gaussian RBF kernel on a compact ...

BACKGROUND The relationship between surgical margin and local recurrence (LR) in osteosarcoma patients with poor responses to chemotherapy is unclear. Moreover, the incidences of LR according to three different resection planes (bone, soft tissue, and perineurovascular) are not commonly known. METHODS We evaluated the incidence of LR in three areas. To assess whether there is a role of surgic...

We introduce into the classical Perceptron algorithm with margin a mechanism of unlearning which in the course of the regular update allows for a reduction of possible contributions from “very well classified” patterns to the weight vector. The resulting incremental classification algorithm, called Margin Perceptron with Unlearning (MPU), provably converges in a finite number of updates to any ...

In this article we study the generalization abilities of several classifiers of support vector machine (SVM) type using a certain class of kernels that we call universal. It is shown that the soft margin algorithms with universal kernels are consistent for a large class of classification problems including some kind of noisy tasks provided that the regularization parameter is chosen well. In pa...

Support vector machine (SVM) is an important and fundamental technique in learning. Soft-margin SVM models have stronger generalization performance compared with the hard-margin SVM. Most existing works use hinge-loss function which can be regarded as upper bound of 0–1 loss function. However, it cannot explicitly control number misclassified samples. In this paper, we idea soft-margin propose ...

Achieving complete adaptation of the anterior margin of an auricular prosthesis often presents a challenge. Conventional solutions to this problem address soft tissue movements at static positions and do not necessarily reflect how the margin will adapt throughout a complete range of motion of the mandible and head. A technique is presented for creating an adaptable margin for an auricular pros...

We compare L1 and L2 soft margin support vector machines from the standpoint of positive definiteness, the number of support vectors, and uniqueness and degeneracy of solutions. Since the Hessian matrix of L2 SVMs is positive definite, the number of support vectors for L2 SVMs is larger than or equal to the number of L1 SVMs. For L1 SVMs, if there are plural irreducible sets of support vectors,...

In recent years, structured online discriminative learning methods using second order statistics have been shown to outperform conventional generative and discriminative models in the grapheme-to-phoneme (g2p) conversion task. However, these methods update the parameters by sequentially using N -best hypotheses predicted with the current parameters. Thus, the parameters appearing in early hypot...