• No results found

Expanding Self-organizing Map for Data. Visualization and Cluster Analysis

N/A
N/A
Protected

Academic year: 2021

Share "Expanding Self-organizing Map for Data. Visualization and Cluster Analysis"

Copied!
25
0
0

Loading.... (view fulltext now)

Full text

Loading

Figure

Fig. 1. Two SOMs from 2-dimensional space to 1-dimension. The connected dots indicate a string of neurons, and other dots indicate data.
Fig. 2. A schematic view of two different learning rules. (a). The learning rule for the traditional SOM; (b)
Fig. 3. Illustration of the 3 synthetic data sets, Data Set 1 (Upper), Data Set 2 (Middle) and Data Set 3 (Lower)
Fig. 4. The quantization error (Left) and the topological error (Right) during the learning of the ESOM and the SOM for the first data set.
+4

References

Related documents

The South West Community Care Access Centre (CCAC) gets people the care they need to stay well, heal at home and stay safely in their homes longer. They have provided you with

To compare the burden of statin therapy according to the Third Adult Treatment Panel (ATP-III) and the American College of Cardiology/American Heart Association (ACC/AHA)

In Lesson 1 you read that unbalanced forces cause the motion of an object to change. In this lesson you learned how forces cause motion to change. An object accelerates when

2.3    Defining   the   ICT   Profession    

Failure to communicate with client and confirm instructions Legal Services Commissioner v Brott (Legal Practice) [2011] VCAT 110 (7 February 2011)7. The Tribunal found 4

Nurse leaders need to support in-service education and training for all levels of staff involved in care provision, based on identified needs and continuing education

Animal Feed: Comparability of Food Safety Systems and Import Practices of Foreign Countries , Transcript (Mar. 30, 2011) (statement of Clete Willems) (discussing the potential

The data are expressed as percentages relative to untreated cells (control), which were set at 100 %, and represent the mean ± SEM of two independent experiments, each performed