Kernel methods and the exponential family. Neurocomputing 69 - Canu, Smola - 2006 () Citation Context ... space (RKHS) is equal to the set of all possible discriminant functions of boosting. In this paper, we s
A new derivation of the novelty detection algorithm based on the one class SVM is proposed to illustrate the power of the exponential family model in an RKHS. 展开 关键词: Exponential families Kernel methods Novelty detection DOI: 10.1016/J.NEUCOM.2005.12.009 被引量: 99 ...
在论文《Kernel methods and the exponential family》中,上面的也称为Parametric exponential families,是因为这个是通过参数 θ 确定的分布族。同样地,存在Non-parametric exponential families,即通过Kernel定义的分布族, f 是Natural Parameter, K(x,⋅) 被当作充分统计量,所以可以定义函数族: pf(x)=q0(x)exp...
Reproducing Kernel Hilbert Spaces In Probability and Statistics. ^abcM. Kanagawa, P. Hennig, D. Sejdinovic, and B. K. Sriperumbudur. Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. ^abcdV. I. Bogachev. Gaussian measures. ^M. Lifshits. Lectures on Gaussian ...
comprise heterogeneous datasets that serve as the building blocks of gene regulatory networks which can be fused together using kernel methods. Each dataset is transformed into a symmetric positive semidefinite kernel matrix by means of a kernel function, that is real valued, k(x1, x2) and ...
Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other attractive properties, when training a kernel-based model one is guarante
Until now, it remained an open question whether some advantage could be gained from data re-uploading models, in light of the guarantees of kernel methods. In this work, we introduce a unifying framework for explicit, implicit and data re-uploading quantum models (see Fig. 2). We show ...
Kernel methods build on the fundamental concept of Reproducing Kernel Hilbert Spaces (Aronszajn1950; Giné and Nickl2015) and are widely used in machine learning (Shawe-Taylor and Cristianini2004; Hofmann et al.2008) and related fields, such as system identification (Pillonetto et al.2014). One...
Kernel methods capture nonlinear patterns in the data by mapping the input to higher dimensions where linear models can be applied. A function k:Z×Z→R is non-negative definite (or positive semidefinite) if for any finite subset {z1,…, zn} chosen from Z, the Gram matrix (or kernel ...
Browse State-of-the-Art Datasets Methods More Sign In Kernel Conditional Exponential Family 15 Nov 2017 · Michael Arbel, Arthur Gretton · Edit social preview A nonparametric family of conditional distributions is introduced, which generalizes conditional exponential families using functional parameters in...