Forage饲料
发表于 2025-3-28 15:58:23
Niching-Based Feature Selection with Multi-tree Genetic Programming for Dynamic Flexible Job Shop Sc and by comparing the different methods in a larger experimental setup. The results show that feature selection can generate better rules in most of the cases while also being more efficient to in a production environment.
态度暖昧
发表于 2025-3-28 21:48:30
Correlation Analysis Via Intuitionistic Fuzzy Modal and Aggregation Operatorsity and possibility modal operators along with intuitionistic fuzzy t-norms and t-conorms are investigated by verifying the conditions under which A-CC preserve the main properties related to conjugate and complement operations performed on A-IFS.
断言
发表于 2025-3-28 23:23:45
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遗传
发表于 2025-3-29 03:48:23
Towards a Class-Aware Information Granulation for Graph Embedding and Classificationormance improvements when considering also the ground-truth class labels in the information granulation procedure. Furthermore, since the granulation procedure is based on random walks, it is also very appealing in Big Data scenarios.
homeostasis
发表于 2025-3-29 10:27:24
Deep Convolutional Neural Network Processing of Images for Obstacle Avoidancein the lab by a human operator. The network learned the correct responses of left, right, or straight for each of the images with a very low error rate when checked on test images. In addition, ten tests on the actual robot showed that it could successfully and consistently drive through the lab while avoiding obstacles.
companion
发表于 2025-3-29 14:52:42
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STERN
发表于 2025-3-29 16:09:37
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凶猛
发表于 2025-3-29 20:11:08
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CREST
发表于 2025-3-30 02:15:40
https://doi.org/10.1007/978-3-642-69591-9n opens doors for a sampling version of the algorithm, which we call CVaR Q-learning. In order to allow optimizing CVaR on large state spaces, we also formulate loss functions that are later used in a deep learning context. Proposed methods are theoretically analyzed and experimentally verified.
contrast-medium
发表于 2025-3-30 07:37:40
CVaR Q-Learningn opens doors for a sampling version of the algorithm, which we call CVaR Q-learning. In order to allow optimizing CVaR on large state spaces, we also formulate loss functions that are later used in a deep learning context. Proposed methods are theoretically analyzed and experimentally verified.