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Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones

Publications associées (32)

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Learning Near-optimal Decision Rules for Energy Efficient Building Control

Colin Neil Jones

Recent studies suggest that advanced optimization based control methods such as model predictive control (MPC) can increase energy efficiency of buildings. However, adoption of these methods by industry is still slow, as building operators are used to work ...

2012

Learning Near-optimal Decision Rules for Energy Efficient Building Control

Colin Neil Jones

2012

Open-Ended Learning of Visual and Multi-Modal Patterns

Jie Luo

A common trend in machine learning and pattern classification research is the exploitation of massive amounts of information in order to achieve an increase in performance. In particular, learning from huge collections of data obtained from the web, and us ...

EPFL2011

Open-ended Learning of Visual and Multi-modal Patterns

Jie Luo

Ecole polytechnique fédérale de Lausanne2011

Machine learning for information retrieval

David Grangier

In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...

EPFL2008

Machine Learning for Information Retrieval

David Grangier

École Polytechnique Fédérale de Lausanne2008

Machine Learning for Information Retrieval

David Grangier

IDIAP2008

Classification of dog barks: a machine learning approach

Frédéric Kaplan

In this study we analyzed the possible context-specific and individual-specific features of dog barks using a new machine-learning algorithm. A pool containing more than 6,000 barks, which were recorded in six different communicative situations was used as ...

2008

Approximate Bayesian Gaussian process (GP) classification techniques are powerful non-parametric learning methods, similar in appearance and performance to support vector machines. Based on simple probabilistic models, they render interpretable results and ...

Massachusetts Institute of Technology Press2003

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000