Theoretical Analyses on 2-Norm-Based Multiple Kernel Regressors

Theoretical Analyses on 2-Norm-Based Multiple Kernel Regressors

Theoretical Analyses on Ensemble and Multiple Kernel Regressors

For the last few decades, learning based on multiple kernels, such as the ensemble kernel regressor and the multiple kernel regressor, has attracted much attention in the field of machine learning. Although its efficacy was revealed numerically in many works, its theoretical ground is not investigated sufficiently. In this paper, we discuss regression problems with a class of kernels whose corr...

A Multiple Kernel Learning Model Based on p-Norm

By utilizing kernel functions, support vector machines (SVMs) successfully solve the linearly inseparable problems. Subsequently, its applicable areas have been greatly extended. Using multiple kernels (MKs) to improve the SVM classification accuracy has been a hot topic in the SVM research society for several years. However, most MK learning (MKL) methods employ L1-norm constraint on the kerne...

`p-Norm Multiple Kernel Learning

Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations to support interpretability and scalability. Unfortunately, this `1-norm MKL is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtur...

lp-Norm Multiple Kernel Learning

Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations to support interpretability and scalability. Unfortunately, this l1-norm MKL is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtur...