In the last few years, application of Support Vector Machines (SVMs) for solving classification and regression problems has increased, in particular, due to its high generalization performance and its ability to model non-linear relationships. The latter can only be realised if a suitable kernel function is applied. This kernel function transforms the non-linear input space into a high dimensio...

A kernel orthogonal subspace projection (KOSP) algorithm has been developed for nonlinear approximating subpixel proportion in this paper. The algorithm applies linear regressive model to the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum mean squared-error regressor. The algorithm includes two steps: the first step is to select the feat...

One commonly finds in applications of smooth radial basis functions (RBFs) that scaling the kernels so they are ‘flat’ leads to smaller discretization errors. However, the direct numerical approach for computing with flat RBFs (RBF-Direct) is severely ill-conditioned. We present an algorithm for bypassing this ill-conditioning that is based on a new method for rational approximation (RA) of vec...

Many practical engineering applications require the usage of accurate automatic decision systems, usually operating under tight computational constraints. Support Vector Machines (SVMs) endowed with a Radial Basis Function (RBF) as kernel are broadly accepted as the current state of the art for decision problems, but require cross-validation to select the free parameters, which is computational...

The image-based plant identification challenge was focused on tree, herbs and ferns species identification based on different types of images. The aim of the task was to produce relevant species for each observation of a plant of the test dataset. We have elaborated a viewpoints combined classification method for this challenge. We have applied dense SIFT for feature detection and description; ...

Predicting reservoir water levels helps manage droughts and floods. level is complex because it depends on factors such as climate parameters human intervention. Therefore, predicting needs robust models. Our study introduces a new model for levels. An extreme learning machine, the multi-kernel least square support vector machine (MKLSSVM), developed to predict of in Malaysia. The also novel op...

Abstract The growth of urbanization in Klang District was considered to be fast and has increased the concern policy makers town planners. This paper assess changes urban development using Support Vector Machine (SVM) classification by different kernel for purpose studying built up area within year 2017 2021. At initial stage image processing, Land Use Cover (LULC) been classified based on use ...

We present an approach for learning an anisotropic RBF kernel in a game theoretical setting where the value of the game is the degree of separation between positive and negative training examples. The method extends a previously proposed method (KOMD) to perform feature re-weighting and distance metric learning in a kernel-based classification setting. Experiments on several benchmark datasets ...

A numerical method is proposed to approximate the inverse of a general bi-Lipschitz nonlinear dimensionality reduction mapping, where the forward and consequently the inverse mappings are only explicitly defined on a discrete dataset. A radial basis function (RBF) interpolant is used to independently interpolate each component of the high-dimensional representation of the data as a function of ...

© 2009 Dong Won Kim et al. 565 This paper concerns the use of support vector regression (SVR), which is based on the kernel method for learning from examples, in identification of walking robots. To handle complex dynamics in humanoid robot and realize stable walking, this paper develops and implements two types of reference natural motions for a humanoid, namely, walking trajectories on a flat...