Kernel methods and deep learning are two of the most currently remarkable machine
learning techniques that have achieved great success in many applications. Kernel methods …

Multiple kernel clustering (MKC) algorithm aims to group data into different categories by
optimally integrating information from a group of pre-specified kernels. Though …

Deep neural networks and Multiple Kernel Learning are representation learning
methodologies of widespread use and increasing success. While the former aims at learning …

Multiple Kernel Learning is a recent and powerful paradigm to learn the kernel function from
data. In this paper, we introduce MKLpy, a python-based framework for Multiple Kernel …

Depression is one of the principal mental disorders worldwide, yet very few people receive
the appropriate care needed due to the difficulty involved in diagnosing it correctly. Social …

The Multiple Kernel Learning (MKL) paradigm aims at learning the representation from data
reducing the effort devoted to the choice of the kernel's hyperparameters. Typically, the …

Background To help clinicians provide timely treatment and delay disease progression, it is
crucial to identify dementia patients during the mild cognitive impairment (MCI) stage and …

Abstract The recently proposed Minimal Complexity Machine (MCM) learns a hyperplane
classifier by minimizing a bound on the Vapnik-Chervonenkis (VC) dimension. Both the …

Depression is rapidly becoming one of the most common illnesses worldwide, currently
affecting a significant number of people. These people may show different signs of …

Recent work in distance metric learning has significantly improved the performance in k-
nearest neighbor classification. However, the learned metric with these methods cannot …