Download Artificial Neural Networks — ICANN 2001: International by Stephen Grossberg (auth.), Georg Dorffner, Horst Bischof, PDF

By Stephen Grossberg (auth.), Georg Dorffner, Horst Bischof, Kurt Hornik (eds.)

This publication is predicated at the papers awarded on the foreign convention on Arti?cial Neural Networks, ICANN 2001, from August 21–25, 2001 on the - enna college of expertise, Austria. The convention is prepared via the A- trian examine Institute for Arti?cal Intelligence in cooperation with the development popularity and photograph Processing workforce and the heart for Computational - telligence on the Vienna college of know-how. The ICANN meetings have been initiated in 1991 and became the foremost ecu assembly within the ?eld of neural networks. From approximately three hundred submitted papers, this system committee chosen 171 for e-book. every one paper has been reviewed through 3 application committee m- bers/reviewers. we wish to thank all of the individuals of this system comm- tee and the reviewers for his or her nice e?ort within the reviewing procedure and aiding us to establish a scienti?c application of top quality. furthermore, now we have invited 8 audio system; 3 in their papers also are integrated within the complaints. we want to thank the eu Neural community Society (ENNS) for his or her aid. We recognize the ?nancial aid of Austrian airways, A- trian technological know-how origin (FWF) less than the agreement SFB 010, Austrian Society ¨ for Arti?cial Intelligence (OGAI), financial institution Austria, and the Vienna conference Bureau. we wish to precise our honest because of A. Flexer, W. Horn, ok. Hraby, F. Leisch, C. Schittenkopf, and A. Weingessel. The convention and the court cases wouldn't have been attainable with out their huge, immense contri- tion.

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Additional resources for Artificial Neural Networks — ICANN 2001: International Conference Vienna, Austria, August 21–25, 2001 Proceedings

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This will simplify some of the calculations described in Section 3 below. Hessian. , ∂w (HF )ij = ∂ 2 F(w) . ∂wi ∂wj (2) For a neural network as defined above, we abbreviate H ≡ HL◦M◦N . The Hessian ¯ is obtained by taking the expectation of H over proper, which we denote H, ¯ ≡ H . For matching loss functions, HL◦M = AJM = J A . inputs: H M x Fisher Information. , (FF )ij = ∂F(w) ∂F(w) . ∂wi ∂wj (3) Note that FF always has rank one. As before, we abbreviate F ≡ FL◦M◦N . The Fisher information matrix proper, F¯ ≡ F x , describes the geometric structure of weight space [7] and is used in the natural gradient descent approach [8].

11. S. Singhal and L. Wu, “Training multilayer perceptrons with the extended Kalman filter,” in Adv. Neural Info. Proc. Systems: Proc. 1988 Conf. (D. S. ), pp. 133–140, Morgan Kaufmann, 1989. 12. M. E. Harmon and L. C. Baird III, “Multi-player residual advantage learning with general function approximation,” Tech. Rep. WL-TR-1065, Wright Laboratory, WL/AACF, 2241 Avionics Circle, Wright-Patterson AFB, OH 45433-7308, 1996. Architecture Selection in NLDA Networks Jos´e R. Dorronsoro, Ana M. Gonz´ alez, and Carlos Santa Cruz Dept.

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