In this paper we construct the linear support vector machine (SVM) based on the nonlinear rescaling (NR) methodology (see [11, 14, 12] and references therein). The formulation of the linear SVM based on the NR method leads to an algorithm which reduces the number of support vectors without compromising the classification performance compared to the linear soft-margin SVM formulation. The NR alg...

The aim of any prosthodontic treatment plan is to restore the lost or missing tissue with prosthesis. This prosthesis should be compatible with existing hard and soft tissues and fulfill the functional and aesthetic requirements as well. It is important for the treating clinician to be knowledgeable about the interactions of these prosthodontic appliances with oral tissues and consequences of v...

In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme, which enables the conventional confidence-weighted learning method to handle non-separable cases. Unlike the previous confidence-weighted learning algorithms, the proposed soft confidence-weighted learning method enjoys all the four salient properties: (i) large margin training, (ii) confidence weighting, (ii...

In this paper we discuss boosting algorithms that maximize the soft margin of the produced linear combination of base hypotheses. LPBoost is the most straightforward boosting algorithm for doing this. It maximizes the soft margin by solving a linear programming problem. While it performs well on natural data, there are cases where the number of iterations is linear in the number of examples ins...

The large volume principle proposed by Vladimir Vapnik, which advocates that hypotheses lying in an equivalence class with a larger volume are more preferable, is a useful alternative to the large margin principle. In this paper, we introduce a clustering model based on the large volume principle called maximum volume clustering (MVC), and propose two algorithms to solve it approximately: a sof...

In this note, we describe the sequential minimal optimization (SMO) algorithm to solve the soft margin support vector machine (SVM) binary classification problem, which is to be implemented as part of the miniproject. In order to understand our arguments here, the reader is advised to study the SVM chapter of the course notes, in particular the “Solving the Support Vector Machine” section (the ...

BACKGROUND This study was performed to evaluate the usefulness of high-resolution grayscale and color Doppler ultrasound to distinguish benign from malignant soft-tissue masses on the basis of ultrasonographic patterns. METHODS We enrolled 398 female and 420 male patients aged 1-104 years (mean, 49.8 years). All presented with a palpable nodule or mass located superficially in the body. Each ...

BACKGROUND Zygomycetes cause different patterns of infection in immunosuppressed individuals, including sino-orbito-cerebral, pulmonary, skin/soft tissue infection and disseminated disease. Infections with Zygomycetes have a high mortality rate, even with prompt treatment, which includes anti-fungal agents and surgical debridement. In some centers, clear margins are monitored by serial frozen s...