Pattern classiication may be viewed as an ill-posed, inverse problem to which the method of regularization be applied. In doing so, a proper theoretical framework is provided for the application of radial basis function (RBF) networks to pattern classiication, with strong links to the classical kernel regression estimator (KRE)-based classiiers that estimate the underlying posterior class densi...

This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used...

Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military civilian fields. With the continuous enrichment extensive expansion of application scenarios, safety UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones improve drone safety. We deployed a one-class kernel extreme learning machine (OC...

Statistical downscaling is an effort to link global scale to local scale variable. It uses GCM model which usually used as a prime instrument in learning system of various climate. The purpose of this study is as a SD model by using SVR in order to predict the rainfall in dry season; a case study at Indramayu. Through the model of SD, SVR is created with linear kernel and RBF kernel. The result...

Support vector machine (SVM) was the first proposed kernel-based method. It uses a kernel function to transform data from input space into a high-dimensional feature space in which it searches for a separating hyperplane. SVM aims to maximise the generalisation ability that depends on the empirical risk and the complexity of the machine. SVM has been widely adopted in real-world applications in...

An optimized neural network classification method based on kernel holistic learning and division (KHLD) is presented. The proposed the learned radial basis function (RBF) as research object. here can be considered a subspace region consisting of same pattern category in training sample space. By extending space original instances, relevant information between instances obtained from subspace, c...

We use methods from the Fock space and Segal–Bargmann theories to prove several results on Gaussian RBF kernel in complex analysis. The latter is one of most used kernels modern machine learning support vector classification algorithms. Complex analysis techniques allow us consider notions linked radial basis function (RBF) kernels, such as feature map, using so-called transform. also show how ...

The recently proposed “generalized min-max” (GMM) kernel [9] can be efficiently linearized, with direct applications in large-scale statistical learning and fast near neighbor search. The linearized GMM kernel was extensively compared in [9] with linearized radial basis function (RBF) kernel. On a large number of classification tasks, the tuning-free GMM kernel performs (surprisingly) well comp...

In this paper we present an integer programming formulation of the minimum sphere covering problem that seeks to construct a minimum number of spheres to represent the training data. Using soft threshold functions, we further derive a linear programming problem whose solution gives rise to radial basis function classifiers and sigmoid function classifiers. In contrast to traditional RBF and sig...

In this paper, basing our considerations on kernel-based approaches, we propose a new strategy allowing to approximate the prostate cancer dynamics. In particular, starting from several measurements of a specific biomarker, we estimate the tumor growth rate. To achieve this aim, we pre-process data via Radial Basis Function (RBF) interpolation. A careful choice of the basis function and of its ...