通过信息力量估计辐射基础功能网络中心.
Edilson Sousa Júnior1, Antônio Freitas1, Ricardo Rabelo1
1Technology Center, Universidade Federal do Piauí, Teresina 64049-550, PI, Brazil.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究引入了一种新的渐变算法,用于确定使用信息力量的辐射基函数网络中心,改进数据分类和异常检测. 该方法在各种复杂数据集中表现出强的性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 确定辐射基函数网络 (RBFN) 的最佳中心仍然是机器学习的一个重大挑战.
- 有效的聚类和异常值检测对于准确的数据分类和分析至关重要.
研究的目的:
- 提出和评估一种新的梯度算法,用于使用信息力量确定RBFN集群中心.
- 根据信息潜力开发一种可靠的异常值分类方法.
- 评估拟议的算法在多样化和具有挑战性的数据集上的性能.
主要方法:
- 使用梯度算法来识别基于对数据点起作用的信息力量的集群中心.
- 使用信息潜力来确定分类异常值的门.
- 拟议的RBFN方法与标准k-means集群算法进行了基准测试.
主要成果:
- 拟议的梯度算法成功地确定了RBFN的集群中心.
- 信息潜力值有效地识别和分类异常值.
- 结合方法与k-means的RBFN相比显示出更高的性能,特别是在不同集群大小,重叠和噪声的数据集上.
结论:
- 信息力驱动梯度算法为RBFN中心确定提供了有效的解决方案.
- 开发的异常值分类方法提高了RBFN的稳定性.
- 这种方法为数据分类任务提供了有竞争力的替代方案,在复杂的场景中优于传统方法.
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