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2001


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Diffusion of 23Na and 35K in the eutectic melt Na0.32K0.69

Feinauer, A., Majer, G.

{Physical Review B}, 64, 2001 (article)

mms

[BibTex]

2001


[BibTex]


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AMOC studies of positronium in fine MgO powder

van Waeyenberge, B., Dauwe, C., Stoll, H.

In Positron Annihilation. Proceedings of the 12th International Conference on Positron Annihilation, 363/365, pages: 401-403, Materials Science Forum, Trans Tech Publications Ltd., München, 2001 (inproceedings)

mms

[BibTex]

[BibTex]


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Influence of nanocrystallization on the evolution of domain patterns and the magnetoimpedance effect in amorphous Fe73.5Cu1Nb3Si13.5B9 ribbons

Guo, H. Q., Kronmüller, H., Dragon, T., Cheng, Z. H., Shen, B. G.

{Journal of Applied Physics}, 89(1):514-520, 2001 (article)

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[BibTex]

[BibTex]


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Ab-initio statistical mechanics for the phase diagram of NiAl including the effect of vacancies

Lechermann, F., Fähnle, M.

{Physica Status Solidi (B)}, 224, pages: R4-R6, 2001 (article)

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[BibTex]

[BibTex]


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Atomic defects and electronic structure of B2-FeAl, CoAl and NiAl

Fähnle, M., Meyer, B., Bester, G., Majer, J., Börnsen, N.

In Proceedings of DIMAT 2000, the Fifth International Conference on Diffusion in Materials, 194/199, pages: 279-285, Defect and Diffusion Forum, Scitec Publications Ltd., Paris, France, 2001 (inproceedings)

mms

[BibTex]

[BibTex]

1995


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A kendama learning robot based on a dynamic optimization theory

Miyamoto, H., Gandolfo, F., Gomi, H., Schaal, S., Koike, Y., Osu, R., Nakano, E., Kawato, M.

In Preceedings of the 4th IEEE International Workshop on Robot and Human Communication (RO-MAN’95), pages: 327-332, Tokyo, July 1995, clmc (inproceedings)

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[BibTex]

1995


[BibTex]


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Memory-based neural networks for robot learning

Atkeson, C. G., Schaal, S.

Neurocomputing, 9, pages: 1-27, 1995, clmc (article)

Abstract
This paper explores a memory-based approach to robot learning, using memory-based neural networks to learn models of the task to be performed. Steinbuch and Taylor presented neural network designs to explicitly store training data and do nearest neighbor lookup in the early 1960s. In this paper their nearest neighbor network is augmented with a local model network, which fits a local model to a set of nearest neighbors. This network design is equivalent to a statistical approach known as locally weighted regression, in which a local model is formed to answer each query, using a weighted regression in which nearby points (similar experiences) are weighted more than distant points (less relevant experiences). We illustrate this approach by describing how it has been used to enable a robot to learn a difficult juggling task. Keywords: memory-based, robot learning, locally weighted regression, nearest neighbor, local models.

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link (url) [BibTex]

link (url) [BibTex]