2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) 2016
DOI: 10.1109/devlrn.2016.7846796
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Abstract: Abstract-This paper discusses lexicon word learning in highdimensional meaning spaces from the viewpoint of referential uncertainty. We investigate various state-of-the-art Machine Learning algorithms and discuss the impact of scaling, representation and meaning space structure. We demonstrate that current Machine Learning techniques successfully deal with high-dimensional meaning spaces. In particular, we show that exponentially increasing dimensions linearly impact learner performance and that referential un… Show more

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Cited by 3 publications
(3 citation statements)
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References 26 publications
(30 reference statements)
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“…Grounded language learning has often focused on the acquisition of categories and concepts [15], for example in the spatial language [16] and color domains [17]. In that work, centroid-based classifiers with distance (Voronoi tessellation) or other Machine Learning algorithms (neural networks, Bayesian models etc.)…”
Section: Related Workmentioning
confidence: 99%
“…Grounded language learning has often focused on the acquisition of categories and concepts [15], for example in the spatial language [16] and color domains [17]. In that work, centroid-based classifiers with distance (Voronoi tessellation) or other Machine Learning algorithms (neural networks, Bayesian models etc.)…”
Section: Related Workmentioning
confidence: 99%
“…KNN is the only tested algorithm for continuous meaning cross-situational learning [19]. Centroid algorithms are the goto method for interactive learning [14], [15], [20].…”
Section: Learning Algorithmsmentioning
confidence: 99%
“…In interactive learning researchers often propose to use prototypes for the learner's lexicon [14], [15], [20] and to update these prototypes iteratively as new samples of word object mappings are collected by the learner. a) Representation: The learner stores prototypes p ∈ R 3 .…”
Section: B Prototype Estimation (Pe)mentioning
confidence: 99%