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Titlebook: Neural Nets WIRN Vietri-99; Proceedings of the 1 Maria Marinaro,Roberto Tagliaferri Conference proceedings 1999 Springer-Verlag London Limi

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樓主: Pierce
11#
發(fā)表于 2025-3-23 10:49:46 | 只看該作者
Theory, Implementation, and Applications of Support Vector Machinescal properties of SVMs, then present an implementation of SVMs able to work with training sets of very large size. Finally, we discuss two computer vision applications in which SVMs for both pattern recognition and regression estimation have been successfully employed.
12#
發(fā)表于 2025-3-23 13:53:25 | 只看該作者
13#
發(fā)表于 2025-3-23 19:38:04 | 只看該作者
14#
發(fā)表于 2025-3-23 23:37:53 | 只看該作者
15#
發(fā)表于 2025-3-24 05:18:43 | 只看該作者
16#
發(fā)表于 2025-3-24 09:13:59 | 只看該作者
Continual Prediction using LSTM with Forget Gatesa weakness of LSTM networks processing continual input streams without explicitly marked sequence ends. Without resets, the internal state values may grow indefinitely and eventually cause the network to break down. Our remedy is an adaptive “forget gate” that enables an LSTM cell to learn to reset
17#
發(fā)表于 2025-3-24 11:25:11 | 只看該作者
18#
發(fā)表于 2025-3-24 15:57:56 | 只看該作者
19#
發(fā)表于 2025-3-24 22:59:28 | 只看該作者
Online Learning with Adaptive Local Step Sizesernative to their approach by extending Sutton’s work on linear systems to the general, nonlinear case. The resulting algorithms are computationally little more expensive than other acceleration techniques, do not assume statistical independence between successive training patterns, and do not requi
20#
發(fā)表于 2025-3-25 01:24:50 | 只看該作者
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