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Designing constructive machine learning models based on generalized linear learning techniques

Boekbijdrage - Boekhoofdstuk Conferentiebijdrage

We propose a general framework for designing machine learning models that deal with constructing complex structures in the output space. The goal is to provide an abstraction layer to easily represent and design constructive learning models. The learning approach is based on generalized linear training techniques, and exploits techniques from combinatorial optimization to deal with the complexity of the underlying inference required in this type of models. This approach also allows to consider global structural characteristics and constraints over the output elements in an efficient training and prediction setting. The use case focuses on building spatial meaning representations from text to instantiate a virtual world.
Boek: Proceedings of the NIPS workshop on constructive machine learning
Pagina's: 1 - 5
Jaar van publicatie:2013