We present a novel 2D/3D deformable registration method, called Registration Efficiency and Accuracy through Learning Metric on Shape (REALMS), that can support real-time Image-Guided Radiation Therapy (IGRT ). The method consists of two stages: planning-time learning and registration. In the planning-time learning, it firstly models the patient’s 3D deformation space from the patient’s time-va...

<span id="docs-internal-guid-10508d4e-7fff-5011-7a0e-441840e858c8"><span>This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial in hepatitis classification. These two functions were chosen due to their popularity any kernel-based machine learning method for solving classification task. The dataset then used evaluate performance of both method...

In this paper we investigate the use of compactly supported RBF kernels for nonlinear function estimation with LS-SVMs. The choice of compact kernels recently proposed by Genton may lead to computational improvements and memory reduction. Examples however illustrate that compactly supported RBF kernels may lead to severe loss in generalization performance for some applications, e.g. in chaotic ...

In this paper, a new kernel function is introduced that improves the classification accuracy of support vector machines (SVMs) for both linear and non-linear data sets. The proposed kernel function, called Gauss radial basis polynomial function (RBPF) combine both Gauss radial basis function (RBF) and polynomial (POLY) kernels. It is shown that the proposed kernel converges faster than the RBF ...

We investigate a two-dimensional statistical model of N charged particles interacting via logarithmic repulsion in the presence of an oppositely charged compact region K whose charge density is determined by its equilibrium potential at an inverse temperature corresponding toβ= 2. When the charge on the region, s , is greater than N , the particles accumulate in a neighborhood of the boundary o...

The Support Vector Machine (SVM) is a powerful classification technique that has been used extensively in the field of medical imaging. A model based on SVM with Gaussian RBF kernel is proposed here for the automatic detection of brain tumor from MRI images. Various textural characteristics of the MRI images of human brain are extracted to construct a feature set. These features sets are then u...

Ensemble learning algorithms such as boosting can achieve better performance by averaging over the predictions of some base hypotheses. Nevertheless, most existing algorithms are limited to combining only a finite number of hypotheses, and the generated ensemble is usually sparse. Thus, it is not clear whether we should construct an ensemble classifier with a larger or even an infinite number o...

Scrapeoff Layer (SOL) data from DIII-D and C-Mod have been acquired and analyzed for radial particle transport based on a particle balance model. This has allowed a detailed comparison for L-mode plasmas. The inferred radial particle flux, Γ⊥(r), is parameterized in terms of diffusive [Deff(r) ≡ Γ⊥(r)/∇n(r)] and convective particle transport [veff(r) ≡ Γ⊥(r)/n(r)]. The magnitude of the inferred...

In many cases, multivariate interpolation by smooth radial basis functions converges towards polynomial interpolants, when the basis functions are scaled to become “wide”. In particular, examples show that interpolation by scaled Gaussians seems to converge towards the de Boor/Ron “least” polynomial interpolant. The paper starts by providing sufficient criteria for the convergence of radial int...

The choice of the optimal spherical radial basis function (SRBF) in local gravity field modelling from terrestrial gravity data is investigated. Various types of SRBFs are considered: the point-mass kernel, radial multipoles, Poisson wavelets, and the Poisson kernel. The analytical expressions for the Poisson kernel, the point-mass kernel and the radial multipoles are well known, while for the ...