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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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发表于 2025-3-21 19:47:21 | 显示全部楼层 |阅读模式
期刊全称Artificial Neural Networks in Pattern Recognition
期刊简称4th IAPR TC3 Worksho
影响因子2023Friedhelm Schwenker,Neamat Gayar
视频video
发行地址Fast track conference proceeding.Unique visibility.State of the art research
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Artificial Neural Networks in Pattern Recognition; 4th IAPR TC3 Worksho Friedhelm Schwenker,Neamat Gayar Conference proceedings 2010 Spring
Pindex Conference proceedings 2010
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https://doi.org/10.1007/978-3-663-04838-1 were used to evaluate hidden Markov models. The best single model with features of principal component analysis in the region face achieved a detection rate of 76.4 %. To improve these results further, two different fusion approaches were evaluated. Thus, the best fusion detection rate in this study was 86.1 %.
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A New Monte Carlo-Based Error Rate Estimatoring process that generates the data and exploits these models in a Monte Carlo style to provide two biased estimators whose best combination is determined by an iterative solution. We test our estimator against state of the art estimators and show that it provides a reliable estimate in terms of mean-square-error.
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Sur le théorème de préparation de Weierstraßnto the online-learning rules. We provide the mathematical foundation of the respective framework. This framework includes usual gradient descent learning of prototypes as well as parameter optimization and relevance learning for improvement of the performance.
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https://doi.org/10.1007/978-3-663-04838-1detection of three areas in the image corresponding roughly to left and right eyes and mouths. Then, three local networks localize, in these areas, 9 key points per eye and 10 key points on the mouth. Thorough experiments on 3500 images from standard databases (Feret, BioID) show the detector accuracy, its generalization ability and speed.
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Correlation-Based and Causal Feature Selection Analysis for Ensemble Classifiersm can eliminate more redundant and irrelevant features, provides slightly better accuracy and less complexity than causal feature selection. Ensemble using Bagging algorithm can improve accuracy in both correlation-based and causal feature selection.
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