并排上下
发表于 2025-3-28 17:11:19
Granular Instances Selection for Fuzzy Modelinge largely studied especially in the classification problem. However, little work has been done to implement instances selection in fuzzy modeling application. In this paper, we present a framework for fuzzy modeling using the granular instances selection. This method is based on the information gran
Frisky
发表于 2025-3-28 19:56:45
Parameter-Free Anomaly Detection for Categorical Dataions of expected behaviors. It is a major issue of data mining for discovering novel or rare events, actions and phenomena. We investigate outlier detection from a . data set. The problem is especially challenging because of difficulty in defining a meaningful similarity measure for categorical data
Preserve
发表于 2025-3-29 00:57:58
Fuzzy Semi-supervised Support Vector Machinesining set to learn accurate classifiers. For this, it uses both labelled and unlabelled data for training. It also modulates the effect of the unlabelled data in the learning process. Empirical evaluations showed that by additionally using unlabelled data, FSS-SVM requires less labelled training dat
闹剧
发表于 2025-3-29 05:04:21
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冲击力
发表于 2025-3-29 08:39:21
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混合物
发表于 2025-3-29 12:24:36
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consolidate
发表于 2025-3-29 18:25:08
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轻快带来危险
发表于 2025-3-29 21:17:21
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BINGE
发表于 2025-3-30 01:37:15
Investigation in Transfer Learning: Better Way to Apply Transfer Learning between Agentsthm and combines Case-Based Reasoning (CBR) and Heuristically Accelerated Reinforcement Learning (HARL) techniques..The experiments were made comparing differents approaches of Transfer Learning were actions learned in the acrobot problem can be used to speed up the learning of the policies of stabi
AV-node
发表于 2025-3-30 04:09:30
Exploration Strategies for Learned Probabilities in Smart Terrainlities that different types of objects meet needs, based on objects it has previously explored. This requires a rational strategy for determining which objects to explore next based on distances to objects, prevalence of similar objects, and amount of information the agent expects to gain. We define