Search Results for author: Johan Suykens

Found 5 papers, 1 papers with code

Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings

no code implementations9 Mar 2018 Hussain Kazmi, Johan Suykens, Johan Driesen

Results show that savings of over 40% are possible with collaborative multi-agent systems making use of either expert knowledge or additional sensors with no loss of occupant comfort.

Multi-agent Reinforcement Learning

Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning

no code implementations15 Oct 2014 Emanuele Frandi, Ricardo Nanculef, Johan Suykens

Frank-Wolfe algorithms for convex minimization have recently gained considerable attention from the Optimization and Machine Learning communities, as their properties make them a suitable choice in a variety of applications.

Hybrid Conditional Gradient - Smoothing Algorithms with Applications to Sparse and Low Rank Regularization

no code implementations14 Apr 2014 Andreas Argyriou, Marco Signoretto, Johan Suykens

We study a hybrid conditional gradient - smoothing algorithm (HCGS) for solving composite convex optimization problems which contain several terms over a bounded set.

EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines

1 code implementation4 Mar 2014 Marc Claesen, Frank De Smet, Johan Suykens, Bart De Moor

EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models.

Ensemble Learning

A Risk Minimization Principle for a Class of Parzen Estimators

no code implementations NeurIPS 2007 Kristiaan Pelckmans, Johan Suykens, Bart D. Moor

This paper explores the use of a Maximal Average Margin (MAM) optimality principle for the design of learning algorithms.

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