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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International C Věra K?rková,Yannis Manolopoulos,Ilias Maglogianni Confe

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發(fā)表于 2025-3-21 20:00:15 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Artificial Neural Networks and Machine Learning – ICANN 2018
期刊簡稱27th International C
影響因子2023Věra K?rková,Yannis Manolopoulos,Ilias Maglogianni
視頻videohttp://file.papertrans.cn/163/162641/162641.mp4
學科分類Lecture Notes in Computer Science
圖書封面Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International C Věra K?rková,Yannis Manolopoulos,Ilias Maglogianni Confe
影響因子.This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27.th. International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018...The 139 full and 28 short papers as well as 41 full poster papers and 41 short poster papers presented in these volumes was carefully reviewed and selected from? total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and
Pindex Conference proceedings 2018
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https://doi.org/10.1007/978-3-322-97075-6rage correct recognition rate of LDP on Pollenmonitor dataset is 90.95%, which is much higher than that of other compared pollen recognition methods. The experimental results show that our method is more suitable for the practical classification and identification of pollen images than compared methods.
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Rezeption von Fernsehnachrichten im Wandelof patterns what was unachievable for convolutional layers. The new network concept has been confirmed by verification of its ability to perform typical image affine transformations such as translation, scaling and rotation.
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A Novel Echo State Network Model Using Bayesian Ridge Regression and Independent Component Analysiselve combinations of four other regression models and three different choices of dimensionality reduction techniques, and measure its running time. Experimental results show that our model significantly outperforms other state-of-the-art ESN prediction models while maintaining a satisfactory running time.
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New Architecture of Correlated Weights Neural Network for Global Image Transformationsof patterns what was unachievable for convolutional layers. The new network concept has been confirmed by verification of its ability to perform typical image affine transformations such as translation, scaling and rotation.
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