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Titlebook: Artificial Neural Networks in Pattern Recognition; 4th IAPR TC3 Worksho Friedhelm Schwenker,Neamat Gayar Conference proceedings 2010 Spring

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11#
發(fā)表于 2025-3-23 11:46:30 | 只看該作者
Parallelized Kernel Patch Clusteringnly emphasize on Kernel Fuzzy C-Means and Relational Neural Gas. We show that the computation time of this algorithm is basicly linear, i.e. .(.). Further we statistically evaluate the performance of this meta-algorithm on a real-life dataset, namely the Enron Emails.
12#
發(fā)表于 2025-3-23 15:52:25 | 只看該作者
A Novel Word Spotting Algorithm Using Bidirectional Long Short-Term Memory Neural Networksn of the CTC Token Passing algorithm. We demonstrate that such a system has the potential for high performance. For example, a precision of 95% at 50% recall is reached for the 4,000 most frequent words on the IAM offline handwriting database.
13#
發(fā)表于 2025-3-23 20:59:16 | 只看該作者
Bayesian Learning of Generalized Gaussian Mixture Models on Biomedical Images both contain non-Gaussian characteristics, impossible to model using rigid distributions like the Gaussian. Generalized Gaussian mixture models are robust in the presence of noise and outliers and are more flexible to adapt the shape of data.
14#
發(fā)表于 2025-3-24 01:58:48 | 只看該作者
15#
發(fā)表于 2025-3-24 05:04:11 | 只看該作者
Karl Weierstra? und seine Schule exists. In this paper, we show that these ingredients can be used to embed dynamic textures in low dimensional spaces such that, together with a traversing technique in the low dimensional representation, efficient dynamic texture synthesis can be?obtained.
16#
發(fā)表于 2025-3-24 10:14:17 | 只看該作者
17#
發(fā)表于 2025-3-24 12:05:22 | 只看該作者
18#
發(fā)表于 2025-3-24 16:01:51 | 只看該作者
Gegenstand und Zweck des Berichtes, both contain non-Gaussian characteristics, impossible to model using rigid distributions like the Gaussian. Generalized Gaussian mixture models are robust in the presence of noise and outliers and are more flexible to adapt the shape of data.
19#
發(fā)表于 2025-3-24 20:27:42 | 只看該作者
https://doi.org/10.1007/978-3-663-15998-8ments using benchmark datasets, we show that the KDA criterion has performance comparable with that of the selection criterion based on the SVM-based recognition rate with cross-validation and can reduce computational cost. We also show that the KDA criterion can terminate feature selection stably using cross-validation as a stopping condition.
20#
發(fā)表于 2025-3-25 02:41:38 | 只看該作者
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