Neural Network Visualization Techniques

2006 ◽  
pp. 163-197 ◽  
Author(s):  
Marjorie Darrah
Keyword(s):  
Neural Network ◽  
Network Visualization ◽  
Visualization Techniques

Making the Unseen Visible

2014 ◽  
pp. 277-331
Keyword(s):  
Visual Analytics ◽  
Web Search ◽  
Visual Learning ◽  
Network Visualization ◽  
Knowledge Visualization ◽  
Knowledge Maps ◽  
Tag Cloud ◽  
Visualization Techniques ◽  
Tools And Techniques

Themes and examples examined in this chapter discuss the fast growing field of visualization. First, basic terms: data, information, knowledge, dimensions, and variables are discussed before going into the visualization issues. The next part of the text overviews some of the basics in visualization techniques: data-, information-, and knowledge-visualization, and tells about tools and techniques used in visualization such as data mining, clusters and biclustering, concept mapping, knowledge maps, network visualization, Web-search result visualization, open source intelligence, visualization of the Semantic Web, visual analytics, and tag cloud visualization. This is followed by some remarks on music visualization. The next part of the chapter is about the meaning and the role of visualization in various kinds of presentations. Discussion relates to concept visualization in visual learning, visualization in education, collaborative visualization, professions that employ visualization skills, and well-known examples of visualization that progress science. Comments on cultural heritage knowledge visualization conclude the chapter.

scholarly journals Convolution Neural Network with Selective Multi-Stage Feature Fusion: Case Study on Vehicle Rear Detection

2018 ◽  
Vol 8 (12) ◽  
pp. 2468 ◽  
Author(s):  
Won-Jae Lee ◽  
Dong Kim ◽  
Tae-Koo Kang ◽  
Myo-Taeg Lim
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Vehicle Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Adaboost Algorithm ◽  
Multi Stage ◽  
Assistance Systems ◽  
Visualization Techniques

Vision-based vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain characteristic vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithm using these features to implement a vehicle detector. The experimental results verified that the proposed vehicle detection framework exhibited better performance than previous frameworks.

scholarly journals Application of semantic network visualization as a managerial support instrument in financial analyses

2017 ◽  
Vol 5 (1) ◽  
pp. 112-128
Author(s):  
Helena Dudycz
Keyword(s):  
Semantic Network ◽  
Current Trend ◽  
Behavioral Approach ◽  
Network Visualization ◽  
Easy Access ◽  
Modern Information Technology ◽  
Managerial Support ◽  
Ontological Approach ◽  
Decision Making Processes ◽  
Visualization Techniques

Modern information technology (IT) managerial support systems often utilize visualization as a fundamental form of presenting information. In this context, however, users require easy access and rapid retrieval of not only information, but also knowledge stored by the system. The current trend in research is to identify new methods of graphical presentation that can be used to visualize knowledge. One of the most promising trends in this area is the exploration of the ontological approach to knowledge representation, and the associated semantic network visualization techniques. This paper presents selected aspects of the practical application of semantic network visualization in support of decision-making processes in the narrow context of financial indicators analysis, and in the light of both the rational and the behavioral approach.

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