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2001


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Critical magnetic properties of disordered Cr-rich FeCr alloys

Fischer, S. F., Kaul, S. N., Kronmüller, H.

{Journal of Magnetism and Magnetic Materials}, 226(Sp. Iss. SI):540-541, 2001 (article)

mms

[BibTex]

2001


[BibTex]


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Fast ab initio methods for the calculation of adiabatic spin wave spectra in complex systems

Grotheer, O., Ederer, C., Fähnle, M.

{Physical Review B}, 63(10):100401-100401, 2001 (article)

mms

[BibTex]

[BibTex]


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Hydrogen storage in sonicated carbon materials

Hirscher, M., Becher, M., Haluska, M., Dettlaff-Weglikowska, U., Quintel, A., Duesberg, G. S., Choi, Y. M., Downes, P., Hulman, M., Roth, S., Stepanek, I., Bernier, P.

{Applied Physics A-Materials Science \& Processing}, 72(2):129-132, 2001 (article)

mms

[BibTex]

[BibTex]


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Co(NH3)2Cl2 and Co(ND3)2Cl2: Order-Disorder Behaviour of N(H,D)3 and Antiferromagnetic Structure

Leineweber, A., Jacobs, H., E\ssmann, P., Allenspach, F., Fauth, F., Fischer, P.

{Zeitschrift f\"ur Anorganische und Allgemeine Chemie}, 627, pages: 2063-2069, 2001 (article)

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[BibTex]

[BibTex]


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Critical magnetic properties of disordered polycrystalline Fe75Fe25 and Cr70Fe30 alloys

Fischer, S. F., Kaul, S. N., Kronmüller, H.

{Physical Review B}, 65, 2001 (article)

mms

[BibTex]

[BibTex]


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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]

[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)

mms

[BibTex]

[BibTex]

1995


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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.

am

link (url) [BibTex]

1995


link (url) [BibTex]