Using Growing Neural Gas in Prototype Generation for Nearest Neighbor Classifiers

2015 ◽  
pp. 276-283 ◽  
Author(s):  
Jussara Dias ◽  
Marcos G. Quiles ◽  
Ana Carolina Lorena
Keyword(s):  
Nearest Neighbor ◽  
Growing Neural Gas ◽  
Neural Gas ◽  
Nearest Neighbor Classifiers

From Point Cloud to Triangular Mesh by Growing Neural Gas

2014 ◽  
Vol 24 (3) ◽  
pp. 651-662
Author(s):  
Feng ZENG ◽  
Tong YANG ◽  
Shan YAO
Keyword(s):  
Point Cloud ◽  
Triangular Mesh ◽  
Growing Neural Gas ◽  
Neural Gas

scholarly journals Semantics of Voids within Data: Ignorance-Aware Machine Learning

2021 ◽  
Vol 10 (4) ◽  
pp. 246
Author(s):  
Vagan Terziyan ◽  
Anton Nikulin
Keyword(s):  
Spatial Data ◽  
Information Science ◽  
Nearest Neighbor ◽  
Geographical Information ◽  
Two Dimensional ◽  
Prototype Selection ◽  
Data Space ◽  
Important Concern ◽  
Traditional Classification ◽  
Nearest Neighbor Classifiers

Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to build a model based on the semantics of our ignorance, i.e., by processing the shape of “voids” within the available data space? Can we improve traditional classification by also modeling the ignorance? In this paper, we provide some algorithms for the discovery and visualization of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance semantics discovery.

Growing Neural Gas Based on Data Density

2018 ◽  
pp. 314-323
Author(s):  
Lukáš Vojáček ◽  
Pavla Dráždilová ◽  
Jiří Dvorský
Keyword(s):  
Growing Neural Gas ◽  
Neural Gas ◽  
Data Density

Large-margin nearest neighbor classifiers via sample weight learning

2011 ◽  
Vol 74 (4) ◽  
pp. 656-660 ◽  
Author(s):  
Qinghua Hu ◽  
Pengfei Zhu ◽  
Yongbin Yang ◽  
Daren Yu
Keyword(s):  
Nearest Neighbor ◽  
Sample Weight ◽  
Large Margin ◽  
Weight Learning ◽  
Nearest Neighbor Classifiers

scholarly journals Real-time 3D point cloud segmentation using Growing Neural Gas with Utility

2016 ◽  
Author(s):  
Yuichiro Toda ◽  
Zhaojie Ju ◽  
Hui Yu ◽  
Naoyuki Takesue ◽  
Kazuyoshi Wada ◽  
...  
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
3D Point Cloud ◽  
Growing Neural Gas ◽  
Neural Gas ◽  
Point Cloud Segmentation
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