基于多类图形的大边缘分类器:支持向量和神经网络的统一方法
概括
本研究介绍了加布里埃尔图 (GGs) 用于高级二进制和多类分类. 新的方法提高了准确性和效率,优于以前的GG分类器,并匹配基于树的模型.
科学领域:
- 机器学习 机器学习
- 计算几何学的计算几何学
- 数据挖掘 数据挖掘
背景情况:
- 传统的大边缘分类器源于优化.
- 支向量 (SVs) 也可以通过几何推导.
- 加布里埃尔图 (GGs) 为分类提供了一个几何方法.
研究的目的:
- 介绍使用加布里埃尔图 (GGs) 进行二进制和多类分类的进展.
- 提高基于GG的分类器的性能和效率.
- 为增强图形规范化和计算引入新型组件.
主要方法:
- 推出了Chipclass,这是一个基于GG的二进制分类器,没有超参数和优化.
- 提议更顺的激活功能和结构性SV (SSV) 中心神经元,以改善分类轮.
- 开发了一个新的子图/以距离为基础的会员功能,用于图的规范化.
- 实现了一个更高效的GG再计算算法.
- 扩展的神经网络架构可通过反向传播或线性方程进行训练.
主要成果:
- 提出的方法实现了低概率的边际和更光滑的分类轮.
- 新的GG再计算算法在计算上成本较低.
- 实验结果表明,与以前基于GG的分类器相比,其性能优越.
- 提出的方法显示了与以树为基础的模型的统计等价性.
结论:
- 新的基于GG的方法增强了二进制和多类分类.
- 这些改进提供了更高的效率和准确性.
- 这种几何方法为基于优化和基于树的模型提供了有竞争力的替代方案.
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