With several good research groups actively working in machine learning (ML) approaches, we have now the concept of self-containing machine learning solutions that oftentimes work out-ofthe-box leading to the concept of ML black-boxes. Although it is important to have such blackboxes helping researchers to deal with several problems nowadays, it comes with an inherent problem increasingly more e...

Machine Learning (ML) algorithms have been more prevalent in recent years, and they are being used to tackle complicated issues across a broad range of fields. Wind energy is not an exception, as ML has recently applied wind turbine blade design, wake velocity turbulence intensity prediction, even farm optimization. The immense learning ability models enables them be trained predict regress com...

Author of this paper was coordinator of the Machine Learning project StatLog during 1990-1993. This project was supported nancially by the European Community. The main aim of StatLog was to evaluate diier-ent learning algorithms using real industrial and commercial applications. As an industrial partner and contributor, Daimler-Benz has introduced diierent applications to Stat-Log among them fa...

In this paper, we present the current state-of-the-art of decisionmaking (DM) andmachine learning (ML) and bridge the two research domains to create an integrated approach of complex problem solving based on human and computational agents. We present a novel classification of ML, emphasizing the human-in-the-loop in interactive ML (iML) andmore specific on collaborative interactiveML (ciML), wh...

In Machine Learning (ML), the learning process of an algorithm given a set of evidences is studied via complexity measures. The way towards using ML complexity measures in the Human Learning (HL) domain has been paved by a previous study, which introduced Human Rademacher Complexity (HRC): in this work, we introduce Human Algorithmic Stability (HAS). Exploratory experiments, performed on a grou...

Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).

hStreams is a recently proposed (IPDPSW 2016) task-based target-agnostic heterogeneous streaming library that supports task concurrency over heterogeneous platforms. We share our experience of enabling a non-trivial machine learning (ML) algorithm: K-nearest neighbor using hStreams. The K-nearest neighbor (KNN) is a popular algorithm with numerous applications in machine learning, data-mining, ...

In this editorial we brie ̄ y discuss interaction between two important areas of arti® cial intelligence: computer vision (CV ) and machine learning (ML ). Although the two ® elds have a long-standing tradition and can be considered technologically mature, past research in applying ML techniques to CV problems has been limited. After a short introduction in the ® elds of computer vision and mach...