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Titlebook: Artificial Neural Networks - ICANN 2006; 16th International C Stefanos D. Kollias,Andreas Stafylopatis,Erkki Oja Conference proceedings 200

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发表于 2025-3-21 19:38:16 | 显示全部楼层 |阅读模式
期刊全称Artificial Neural Networks - ICANN 2006
期刊简称16th International C
影响因子2023Stefanos D. Kollias,Andreas Stafylopatis,Erkki Oja
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Artificial Neural Networks - ICANN 2006; 16th International C Stefanos D. Kollias,Andreas Stafylopatis,Erkki Oja Conference proceedings 200
影响因子This book includes the proceedings of the International Conference on Artificial Neural Networks (ICANN 2006) held on September 10-14, 2006 in Athens, Greece, with tutorials being presented on September 10, the main conference taking place during September 11-13 and accompanying workshops on perception, cognition and interaction held on September 14, 2006. The ICANN conference is organized annually by the European Neural Network Society in cooperation with the International Neural Network Society, the Japanese Neural Network Society and the IEEE Computational Intelligence Society. It is the premier European event covering all topics concerned with neural networks and related areas. The ICANN series of conferences was initiated in 1991 and soon became the major European gathering for experts in these fields. In 2006 the ICANN Conference was organized by the Intelligent Systems Laboratory and the Image, Video and Multimedia Systems Laboratory of the National Technical University of Athens in Athens, Greece. From 475 papers submitted to the conference, the International Program Committee selected, following a thorough peer-review process, 208 papers for publication and presentation to
Pindex Conference proceedings 2006
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Tauwasser im Inneren von Bauteilen, of the respective performance is detected. Results are presented based on the IST HUMAINE NoE naturalistic database; both facial expression information and prosodic audio features are extracted from the same data and feature-based emotion analysis is performed through the proposed adaptive neural network methodology.
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The Land and Residential Patterns,lidean CG descent. Since a drawback of full natural gradient is its larger computational cost, we also consider some cost simplifying variants and show that one of them, diagonal natural CG, also gives better minima than standard CG, with a comparable complexity.
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A Functional Approach to Variable Selection in Spectrometric Problemsster than selecting variables. Moreover, a B-spline coefficient depends only on a limited range of original variables: this preserves interpretability of the selected variables. We demonstrate the interest of the proposed method on real-world data.
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Speeding Up the Wrapper Feature Subset Selection in Regression by Mutual Information Relevance and Rs compared to a stand-alone wrapper approach. Finally, the wrapper takes the bias of the regression model into account, because the regression model guides the search for optimal features. Results are shown for the ‘Boston housing’ and ‘orange juice’ benchmarks based on the multilayer perceptron regression model.
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Adaptive On-Line Neural Network Retraining for Real Life Multimodal Emotion Recognition of the respective performance is detected. Results are presented based on the IST HUMAINE NoE naturalistic database; both facial expression information and prosodic audio features are extracted from the same data and feature-based emotion analysis is performed through the proposed adaptive neural network methodology.
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Dimensionality Reduction Based on ICA for Regression Problemsregression problems by maximizing the joint mutual information between target variable and new features. Using the new features, we can greatly reduce the dimension of feature space without degrading the regression performance.
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