摘要翻译:
在降维和流形学习的实际应用中,自组织网络如神经网络、生长神经网络等都得到了广泛的应用。通常,在这些应用中,自适应网络的结构产生了对输入数据点采样的未知子空间拓扑的良好估计。这里提出的方法采取了不同的观点,即通过假设输入空间是一个已知维数的流形。反过来,这种新型的生长自组织网络获得了自适应能力,可以保证输入流形的精确拓扑结构得到有效和稳定的恢复。
---
英文标题:
《A Growing Self-Organizing Network for Reconstructing Curves and Surfaces》
---
作者:
Marco Piastra
---
最新提交年份:
2008
---
分类信息:
一级分类:Computer Science 计算机科学
二级分类:Neural and Evolutionary Computing 神经与进化计算
分类描述:Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络,连接主义,遗传算法,人工生命,自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
--
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
---
英文摘要:
Self-organizing networks such as Neural Gas, Growing Neural Gas and many others have been adopted in actual applications for both dimensionality reduction and manifold learning. Typically, in these applications, the structure of the adapted network yields a good estimate of the topology of the unknown subspace from where the input data points are sampled. The approach presented here takes a different perspective, namely by assuming that the input space is a manifold of known dimension. In return, the new type of growing self-organizing network presented gains the ability to adapt itself in way that may guarantee the effective and stable recovery of the exact topological structure of the input manifold.
---
PDF链接:
https://arxiv.org/pdf/0812.2969


雷达卡



京公网安备 11010802022788号







