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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2022; 31st International C Elias Pimenidis,Plamen Angelov,Mehmet Aydin Conference p

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https://doi.org/10.1007/978-3-540-48954-2 algorithm is presented to optimize the convex objective function. The results compared with the other classical methods on gas sensor array data sets demonstrate that the proposed method can effectively reduce the number of sensors with higher classification accuracy.
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,Analysing the Predictivity of Features to Characterise the Search Space,or transferring experience across domains, the selection of the most representative features remains crucial. The proposed approach analyses the predictivity of a set of features in order to determine the best categorization.
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,Robust Sparse Learning Based Sensor Array Optimization for Multi-feature Fusion Classification, algorithm is presented to optimize the convex objective function. The results compared with the other classical methods on gas sensor array data sets demonstrate that the proposed method can effectively reduce the number of sensors with higher classification accuracy.
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162656.jpg
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https://doi.org/10.1007/978-3-031-15937-4artificial intelligence; computer networks; computer science; computer systems; computer vision; database
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Schleifbarkeit unterschiedlicher Werkstoffe,edictability is to characterise the search spaces and take actions accordingly. A well-characterised search space can assist in mapping the problem states to a set of operators for generating new problem states. In this paper, a landscape analysis-based set of features has been analysed using the mo
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Schleifbarkeit unterschiedlicher Werkstoffe,t approaches mainly concentrate on aggregating deep features from convolutional networks and introducing edge supervision for a guarantee of compact targets. Though significant progress has been accomplished, the problems of low-contrast by targets against backgrounds and the inconsistency of object
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