An improved evolution-constructed (iECO) features framework: Distribution statement A: Approved for public release; distribution is unlimited

Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine learning. However, a serious limitation of kernel methods is knowing which kernel is needed in practice. Multiple kernel learning (MKL) is an attempt to learn a new tailored kernel through the aggregation of a set of valid known kernels. There are generally three approaches to MKL: fixed rules, heuristics, and optimization. Optimization is the most popular; however, a shortcoming of most optimization approaches is that they are tightly coupled with the underlying objective function and overfitting occurs. Herein, we take a different approach to MKL. Specifically, we explore different divergence measures on the values in the kernel matrices and in the reproducing kernel Hilbert space (RKHS). Experiments on benchmark datasets and a computer vision feature learning task in explosive hazard detection demonstrate the effectiveness and generalizability of our proposed methods.

show abstract

An improved evolution-constructed (iECO) features framework: Distribution statement A: Approved for public release; distribution is unlimited

Cited by 3 publications

References 11 publications

A Comparison of Evolutionary and Neural Attention Modeling Relative to Adversarial Learning

A Comparison of Evolutionary and Neural Attention Modeling Relative to Adversarial Learning

iECO learned matched filters for automatic target recognition in synthetic midwave infrared imagery

Kernel Matrix-Based Heuristic Multiple Kernel Learning

Contact Info

Product

Resources

About