盖他为秘密
发表于 2025-3-28 14:55:27
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LARK
发表于 2025-3-28 21:42:00
0302-9743 s working in all areas of neural network- and machine learning-based pattern recognition to present and discuss the latest research, results, and ideas in these areas... .978-3-319-46181-6978-3-319-46182-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
Fulminate
发表于 2025-3-29 00:49:06
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AWE
发表于 2025-3-29 03:57:05
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bonnet
发表于 2025-3-29 10:31:52
https://doi.org/10.1007/978-3-662-25786-9yper-parameters tuning, CNNs have been scarcely studied in the context of incremental learning where data are available in consecutive batches and retraining the model from scratch is unfeasible. In this work we compare different incremental learning strategies for CNN based architectures, targeting real-word applications.
GROVE
发表于 2025-3-29 14:58:09
On CPU Performance Optimization of Restricted Boltzmann Machine and Convolutional RBMRBM) models and Convolutional RBM (CRBM) models on CPU with the Contrastive Divergence (CD) algorithm. Experimentally, we show that the proposed techniques can reduce the training time by up to 30 times for RBM and up to 12 times for CRBM, on a data set of handwritten digits.
Arboreal
发表于 2025-3-29 15:39:46
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荒唐
发表于 2025-3-29 22:28:25
Conference proceedings 2016cted from 32 submissions for inclusion in this volume. The workshop will act as a major forum for international researchers and practitioners working in all areas of neural network- and machine learning-based pattern recognition to present and discuss the latest research, results, and ideas in these areas... .
无辜
发表于 2025-3-30 00:53:28
,Die Gynäkologie des Truppenarztes,ical systems play an important role. These approaches are empirically assessed on two nontrivial datasets of sequences on a prediction task. Experimental results show that indeed linear dynamical systems can either directly provide a satisfactory solution, as well as they may be crucial for the success of more sophisticated nonlinear approaches.
雄辩
发表于 2025-3-30 06:10:08
Festschrift zum 70. Geburtstageo-training, in which a classifier strengthen another one by feeding it with new labeled data. We propose several co-training strategies to exploit the potential indeterminacy of credal classifiers and test them on several UCI datasets. We then compare the best strategy to the standard co-training process to check its efficiency.