This paper presents the framework of kernel-based methods in the context of hyperspectral image classification, illustrating from a general viewpoint the main characteristics of different kernel-based approaches
we show a connection between boosting and kernel-based methods, highlighting both theoretical and practical applications. In theℓ2context, we show that boosting with a weak learner defined by a kernelKis equivalent to estimation with a specialboosting kernel...
To identify key variables for kernel-based methods, several variable selection approaches have been previously reported in supervised classifications and regressions30,31, however, determination of important variables for unsupervised data using kernel-based methods is still challenging. Here, we describe a...
Kernel-based methods are the mainstream methods for solving data modeling and fault detection of nonlinear systems. However, they also suffer from some problems when applying different kernel functions in practice. The most widely used kernel function is the Gaussian kernel function, as all of the ...
Kernel matrix design and hyperparameter estimation are two core issues for the kernel based regularization methods. In contrast with the former issue, there are few results reported for the latter issue. In this paper, we focused on the latter issue and studied the properties of several hyperpara...
Methods In this work, we proposed a novel link prediction model based on Gaussian kernel-based method and linear optimization algorithm for inferring miRNA–lncRNA interactions (GKLOMLI). Given an observed miRNA–lncRNA interaction network, the Gaussian kernel-based method was employed to output two...
又称有监督的学习方法,主要包括两大类:基于特征向量的学习方法(feature-based)和基于核函数的学习方法(kernel-based… www.cnblogs.com|基于8个网页 3. 内核 KVM 是指基于 Linux内核(Kernel-based)的虚拟机(Virtual Machine) 。KVM 最大的 好处就在于它是与 Linux 内核集成的… ...
The Kernel Methods Toolbox (KMBOX) is a collection of MATLAB programs that implement kernel-based algorithms, with a focus on regression algorithms and online algorithms. It can be used for nonlinear signal processing and machine learning. KMBOX includes implementations of algorithms such as kernel ...
In particular, we show how to design a Gibbs sampler which quickly converges to the target distribution. Numerical simulations show a substantial improvement in the accuracy of the estimates over state-of-the-art kernel-based methods when employed in identification of systems with quantized data....
Conversely, statistical-based methods perform better on categories such as vegetation. Therefore, it is recommended to use different feature extractors and try to combine the advantages between them, which is the basic idea of decision fusion. Decision fusion can be defined as the process of fusing...