Perceivable objects are customarily termed as concepts and their representations (localist-distributed, modality-specific, or experience-dependent) are ingrained in our lives. Despite a considerable amount of computational modeling research focuses on concrete concepts, no comprehensible method for abstract concepts has hitherto been considered. Abstract concepts can be viewed as a blend of concrete concepts. We use this view in our proposed model, Regulated Activation Network (RAN), by learning representations of non-convex abstract concepts without supervision via a hybrid model that has an evolving topology. First, we describe the RAN’s modeling process through a Toy-data problem yielding a performance of 98.5%(ca.) in a classification task. Second, RAN’s model is used to infer psychological and physiological biomarkers from students’ active and inactive states using sleep-detection data. The RAN’s capability of performing classification is shown using five UCI benchmarks, with the best outcome of 96.5% (ca.) for Human Activity recognition data. We empirically demonstrate the proposed model using standard performance measures for classification and establish RAN’s competency with five classifiers. We show that the RAN adeptly performs classification with a small amount of data and simulate cognitive functions like activation propagation and learning.

中文翻译：

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使用受监管的激活网络学习非凸抽象概念

可感知对象通常被称为概念，它们的表征（局部分布的、特定于模态的或依赖于经验的）在我们的生活中根深蒂固。尽管大量的计算建模研究都集中在具体概念上，但迄今为止还没有考虑过抽象概念的可理解方法。抽象概念可以看作是具体概念的混合。我们在我们提出的模型中使用这个视图，调节激活网络 (RAN)，通过学习非凸抽象概念的表示，通过具有不断发展的拓扑的混合模型进行监督。首先，我们通过在分类任务中产生 98.5%(ca.) 的性能的玩具数据问题来描述 RAN 的建模过程。第二，RAN 的模型用于使用睡眠检测数据从学生的活跃和不活跃状态推断心理和生理生物标志物。RAN 执行分类的能力使用五个 UCI 基准来显示，人类活动识别数据的最佳结果为 96.5% (ca.)。我们使用用于分类的标准性能度量凭经验证明了所提出的模型，并建立了 RAN 对五个分类器的能力。我们表明 RAN 能够熟练地使用少量数据进行分类，并模拟激活传播和学习等认知功能。我们使用用于分类的标准性能度量凭经验证明了所提出的模型，并建立了 RAN 对五个分类器的能力。我们表明 RAN 能够熟练地使用少量数据进行分类，并模拟激活传播和学习等认知功能。我们使用用于分类的标准性能度量凭经验证明了所提出的模型，并建立了 RAN 对五个分类器的能力。我们表明 RAN 能够熟练地使用少量数据进行分类，并模拟激活传播和学习等认知功能。