Identification of multimodal human-robot interaction using combined kernels
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2015-12-15
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Abstract
In this paper we propose a methodology to build multiclass classifiers for
the human-robot interaction problem. Our solution uses kernel-based classifiers and
assumes that each data type is better represented by a different kernel. The kernels
are then combined into one single kernel that uses all the dataset involved in the HRI
process. The results on real data shows that our proposal is capable of obtaining lower
generalization errors due to the use of specific kernels for each data type. Also, we
show that our proposal is more robust when presented to noise in either or both data
types.