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Titlebook: Artificial Neural Networks in Pattern Recognition; 5th INNS IAPR TC 3 G Nadia Mana,Friedhelm Schwenker,Edmondo Trentin Conference proceedin

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51#
發(fā)表于 2025-3-30 12:07:17 | 只看該作者
52#
發(fā)表于 2025-3-30 13:23:49 | 只看該作者
53#
發(fā)表于 2025-3-30 19:36:21 | 只看該作者
Teri Tibbett,Michael I. Jeffery election, and political strategies are discussed that target demographically similar geographical regions based on ESOM results. The ESOM and .-means clusterings are compared and found to be dissimilar by the variation of information distance function.
54#
發(fā)表于 2025-3-30 22:56:01 | 只看該作者
Towards a Novel Probabilistic Graphical Model of Sequential Data: A Solution to the Problem of Struc sequence of patterns. Complexity issues are tackled by means of adequate strategies from classic literature on probabilistic graphical models. A preliminary empirical evaluation is presented eventually.
55#
發(fā)表于 2025-3-31 04:28:25 | 只看該作者
56#
發(fā)表于 2025-3-31 07:20:49 | 只看該作者
Using Self Organizing Maps to Analyze Demographics and Swing State Voting in the 2008 U.S. President election, and political strategies are discussed that target demographically similar geographical regions based on ESOM results. The ESOM and .-means clusterings are compared and found to be dissimilar by the variation of information distance function.
57#
發(fā)表于 2025-3-31 09:15:22 | 只看該作者
Herbert Birkhofer,Timo Kümmerlesed for any positive semidefinite data matrix. We demonstrate the superior performance of the technique, kernel RSLVQ, in a variety of benchmarks where results competitive or even superior to state-of-the-art support vector machines are obtained.
58#
發(fā)表于 2025-3-31 17:17:54 | 只看該作者
Ertrag und Risiko einzelner Papiere,er Markov assumptions. Suitable maximum pseudo-likelihood algorithms for learning the parameters of the model from data are then developed. The resulting learning machine is expected to fit scenarios whose nature involves discovering the stochastic (in)dependencies amongst the random variables, and the corresponding variations over time.
59#
發(fā)表于 2025-3-31 19:31:47 | 只看該作者
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