One objective of this study was to provide readers with a clear and unified understanding of parametric statistical and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Furthermore, another objective was to provide a simple and user-friendly R package, named KRMM, which allows users to perform RKHS regression with...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ADAPTIVE FILTERING IN REPRODUCING KERNEL HILBERT SPACES By Weifeng Liu December 2008 Chair: Jose C. Principe Major: Electrical and Computer Engineering The theory of linear adaptive filters has reached maturity, unlike the field of nonli...

We analyze the use, advantages, and drawbacks of graph kernels in chemoin-formatics, including a comparison of kernel-based approaches with other methodology, as well as examples of applications. Kernel-based machine learning [1], now widely applied in chemoinformatics, delivers state-of-the-art performance [2] in tasks like classification and regression. Molecular graph kernels [3] are a recen...

Twin support vector machines (TWSVMs) have been shown to be effective classifiers for a range of pattern classification tasks. However, the TWSVM formulation suffers from shortcomings: (i) uses hinge loss function which renders it sensitive dataset outliers (noise sensitivity). (ii) It requires matrix inversion calculation in Wolfe-dual is intractable datasets with large numbers features/sample...

Within kernel–based interpolation and its many applications, it is a well–documented but unsolved problem to handle the scaling or the shape parameter. We consider native spaces whose kernels allow us to change the kernel scale of a d–variate interpolation problem locally, depending on the requirements of the application. The trick is to define a scale function c on the domain Ω ⊂ Rd to transfo...

Recent research has shown the effectiveness of using sparse coding(Sc) to solve many computer vision problems. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which may reduce the feature quantization error and boost the sparse coding performance, we propose Kernel Sparse Representation(KSR). KSR is essentially the sparse coding technique in a high dime...

Support vector domain description (SVDD) is a useful tool in data mining, used for analysing the within-class distribution of multi-class data and to ascertain membership of a class with known training distribution. An important property of the method is its inner-product based formulation, resulting in its applicability to reproductive kernel Hilbert spaces using the “kernel trick”. This pract...