Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data.
作者： Ashok N. Srivastava
DOI:
关键词: Tree kernel 、 Mixture model 、 Pattern recognition 、 Mixture distribution 、 Artificial intelligence 、 Variable kernel density estimation 、 Kernel (statistics) 、 Computer science 、 Kernel embedding of distributions 、 Probabilistic logic 、 Synthetic data
摘要: This paper presents a method of generating Mercer Kernels from an ensemble probabilistic mixture models, where each model is generated Bayesian density estimate. We show how to convert the estimates into Kernel, describe properties this new kernel function, and give examples performance on unsupervised clustering synthetic data also in domain multispectral image understanding.
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