Support vector machines (SVMs), though accurate, are not preferred in applications
requiring great classification speed, due to the number of support vectors being large. To …

Solutions for learning from large scale datasets, including kernel learning algorithms that
scale linearly with the volume of the data and experiments carried out on realistically large …

Appropriate choice of a kernel is the most important ingredient of the kernel-based learning
methods such as support vector machine (SVM). Automatic kernel selection is a key issue …

Kernel principal component analysis (kernel-PCA) is an elegant nonlinear extension of one
of the most used data analysis and dimensionality reduction techniques, the principal …

Abstract The Support Vector Machine (SVM) is a supervised learning algorithm to analyze
data and recognize patterns. The standard SVM suffers from some limitations in nonlinear …

In this paper, we address a machine learning task using encrypted training data. Our basic
scenario has three parties: Data Owners, who own private data; an Application, which wants …

A truly distributed (as opposed to parallelized) support vector machine (SVM) algorithm is
presented. Training data are assumed to come from the same distribution and are locally …

We devise a boosting approach to classification and regression based on column
generation using a mixture of kernels. Traditional kernel methods construct models based …

In recent years, there has been remarkable growth in Federated Learning (FL) approaches
because they have proven to be very effective in training large Machine Learning (ML) …

Abstract Hidden Markov Models (HMMs) are, undoubtedly, the most employed core
technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from …