Land use is being considered as an element in determining land change studies, environmental planning and natural resource applications. The Earth’s surface Study by remote sensing has many benefits such as, continuous acquisition of data, broad regional coverage, cost effective data, map accurate data, and large archives of historical data. To study land use / cover, remote sensing as an effic...

The classification of diseases appears as one of the fundamental problems for a medical practitioner, which might be substantially improved by intelligent systems. The present work is aimed at designing in what way an intelligent system supporting medical decision can be developed by hybridizing radial basis function neural networks (RBFNs) and differential evolution (DE). To this extent, a two...

where 1 < p < N+2 N−2 , N ≥ 3. It is well-known that this equation has a unique positive radial solution. The existence of sign-changing radial solutions with exactly k nodes is also known. However the uniqueness of such solutions is open. In this paper, we show that such sign-changing radial solution is unique when p is close to N+2 N−2 . Moreover, those solutions are non-degenerate, i.e., the...

The role of width in kernel models and radial-basis function networks is investigated with a special emphasis on the Gaussian case. Quantitative bounds are given on kernel-based regularization showing the effect of changing the width. These bounds are shown to be d-th powers of width ratios, and so they are exponential in the dimension of input data.

A non-adaptive image interpolation algorithm is proposed to scale images for any given scaling factor with an enhancement scheme to ensure a better picture. |In the proposed algorithm, two reference frames, one with higher resolution and another with lower resolution are generated using an original videoframefor the User defined scaling factor. Later, two Intermediate frames are generated from ...

This work describes an auto-calibrated method for parallel imaging with spiral trajectory. The method is a k-space approach where an interpolation kernel, accounting for coil sensitivity factors, is derived from experimental data and used to interpolate the reduced data set in parallel imaging to estimate the missing k-space data. For the case of spiral imaging, this interpolation kernel is def...

Feature extraction is among the most important problems in face recognition sys-tems. In this paper, we propose an enhanced kernel discriminant analysis (KDA)algorithm called kernel fractional-step discriminant analysis (KFDA) for nonlinearfeature extraction and dimensionality reduction. Not only can this new algorithm,like other kernel methods, deal with nonlinearity requir...

In this paper, we propose a method of multiple kernel learning (MKL) to inherently deal with multi-class classification problems. The performances of kernel-based classification methods depend on the employed kernel functions, and it is difficult to predefine the optimal kernel. In the framework of MKL, multiple types of kernel functions are linearly integrated with optimizing the weights for t...

This paper describes the International Institute of Information Technology of Hyderabad’s submission to the task Classification Of Spanish Election Tweets (COSET) as a part of IBEREVAL-2017[1]. The task is to classify Spanish election tweets into political, policy, personal, campaign and other issues. Our system uses Support Vector Machines with radial basis function kernel to classify tweets. ...

This paper presents a comparison between different context-sensitive kernel functions for doing splice site prediction with a support vector machine. Four types of kernel functions will be used: linear-, polynomial-, radial basis functionand negative distance-based kernels. Domain-knowledge can be incorporated into the kernels by incorporating statistical measures or by directly plugging in dis...