Kerneliin

Kerneliin is a term used in machine learning to describe a family of kernel-based models that integrate neural representations with classical kernel methods. The core idea is to learn a feature map that defines a kernel K_theta(x, x') = phi_theta(x) • phi_theta(x'), allowing the model to perform non-linear inference while retaining the analytical advantages of kernel methods, such as principled regularization and the possibility of closed-form solutions in certain settings.

Practically, kerneliin models train a parameterized feature extractor phi_theta, often a small neural network, jointly with a kernel-based predictor such as kernel ridge regression or a large-margin classifier. Training can alternate between updating theta and the predictor, or use end-to-end optimization with gradients flowing through the kernel layer. Regularization is applied to both the feature extractor and the predictor to prevent overfitting.

Advantages include better expressivity than fixed kernels, adaptability to data shifts, compatibility with incremental updates, and applicability to structured data. Limitations center on computational cost, as kernel evaluations can dominate runtime on larger datasets, and on the need for careful tuning of architecture and regularization to maintain generalization.

Applications span bioinformatics, text and graph data, time series, and multimedia, reflecting a broader trend of combining neural networks with kernel methods. The concept is related to ideas from deep kernel learning and scalable support vector machines.

Origin and terminology: kerneliin is a neologism that has appeared in contemporary literature to describe this hybrid approach, emphasizing the integration of neural representations with kernel-based inference.

See also: kernel methods, support vector machines, Gaussian processes, deep kernel learning.