In periodontal practice, root coverage after marginal soft tissue recession requires daily clinical decisions. Numerous longitudinal human studies have been presented to support the efficacy and predictability of different mucogingival surgical techniques for root coverage. Over the years, root coverage procedure using the subepithelial connective tissue graft with variations has emerged as the...

Adequately contoured implant restorations need to transition from the circumferential design of the implant head to the correct cervical tooth anatomy. The implant abutment may be used to effect this transition provided there is sufficient running room. Implant restorations have been described as overcontoured, flat, and undercontoured. It has been shown that overcontouring will generally cause...

The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithm. The minimum classification error (MCE) method and the soft nearest prototype classifier (SNPC) method are two important algorithms using misclassification loss. This paper proposes a new prototype learning algorithm based on the conditional log-likelihood loss (CLL), which is base...

As devices and operating voltages are scaled down, future circuits will be plagued by higher soft error rates, reduced noise margins and defective devices. A key challenge for the future is retaining high reliability in the presence of faulty devices and noise. Probabilistic computing offers one possible approach. In this paper we describe our approach for mapping circuits onto CMOS using princ...

The well-known MinOver algorithm is a simple modification of the perceptron algorithm and provides the maximum margin classifier without a bias in linearly separable two class classification problems. In [1] and [2] we presented DoubleMinOver and MaxMinOver as extensions of MinOver which provide the maximal margin solution in the primal and the Support Vector solution in the dual formulation by...

INTRODUCTION The implantological rehabilitation of the anterior sector is one of the most demanding and complex treatments due to the necessity of obtaining an optimum esthetic result. At the level of the soft tissues, it involves obtaining the complete formation of the papilla and creating a harmonic contour of the gingival margin. OBJECTIVE A bibliographical review has been carried out on t...

The purpose of this paper is to provide a PAC error analysis for the q-norm soft margin classifier, a support vector machine classification algorithm. It consists of two parts: regularization error and sample error. While many techniques are available for treating the sample error, much less is known for the regularization error and the corresponding approximation error for reproducing kernel H...

We continue our previous work on soft margin estimation (SME) to large vocabulary continuous speech recognition (LVCSR) in two new aspects. The first is to formulate SME with different unit separation. SME methods focusing on string-, word-, and phonelevel separation are defined. The second is to compare SME with all the popular conventional discriminative training (DT) methods, including maxim...

salivary  gland  tumors  accounts  for  less  than  1%  of  all  tumors.  minor  salivary  gland  tumors (msgts) are uncommon and make up about 10% of all salivary gland neoplasms. the clinical and histopathological distribution of these tumors vary geographically. a case of a 38 yr old man with a minor salivary gland mass at chin region isreported here. the tumor invaded into the anterior surf...

We present an example-based algorithm for detecting objects in images by integrating component-based classifiers, which automaticaly select the best feature for each classifier and are combined according to the AdaBoost algorithm. The system employs a soft-margin SVM for the base learner, which is trained for all features and the optimal feature is selected at each stage of boosting. We employe...