Abstract
We explore generic mechanisms to introduce structural hints into the method of Unsupervised Kernel Regression (UKR) in order to learn representations of data sequences in a semi-supervised way. These new extensions are targeted at representing a dextrous manipulation task. We thus evaluate the effectiveness of the proposed mechanisms on appropriate toy data that mimic the characteristics of the aimed manipulation task and thereby provide means for a systematic evaluation.
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© 2009 Springer-Verlag Berlin Heidelberg
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Steffen, J., Klanke, S., Vijayakumar, S., Ritter, H. (2009). Towards Semi-supervised Manifold Learning: UKR with Structural Hints. In: PrÃncipe, J.C., Miikkulainen, R. (eds) Advances in Self-Organizing Maps. WSOM 2009. Lecture Notes in Computer Science, vol 5629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02397-2_34
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DOI: https://doi.org/10.1007/978-3-642-02397-2_34
Publisher Name: Springer, Berlin, Heidelberg
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