The immense amount of data created by digitalization requires parallel computing for
machine-learning methods. While there are many parallel implementations for support …

The canonical problem of solving a system of linear equations arises in numerous contexts
in information theory, communication theory, and related fields. In this contribution, we …

The energy consumption of a building has, in recent years, become a determining factor
during its design and construction. With carbon footprints being a growing issue, it is …

The canonical problem of solving a system of linear equations arises in numerous contexts
in information theory, communication theory, and related fields. In this contribution, we …

Abstract Support Vector Machine (SVM) is a powerful classification and regression tool.
Varying approaches, including SVM-based techniques, are proposed for email …

Gaussian belief propagation (GaBP) is an iterative message-passing algorithm for inference
in Gaussian graphical models. It is known that when GaBP converges it converges to the …

This paper proposes a new and efficient parallel implementation of support vector machines
based on decom-position method for handling large scale datasets. The paral-lelizing is …

Abstract Machine learning techniques have facilitated image retrieval by automatically
classifying and annotating images with keywords. Among them support vector machines …

In this paper, we present a novel algorithm for partial intrinsic symmetry detection in 3D
geometry. Unlike previous work, our algorithm is based on a conceptually simple and …

One challenge of predicting building energy consumption is to accelerate model training
when the dataset is very large. This paper proposes an efficient parallel implementation of …