<p><p>This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual
Visual Knowledge Discovery and Machine Learning
โ Scribed by Boris Kovalerchuk
- Publisher
- Springer
- Year
- 2018
- Tongue
- English
- Leaves
- 326
- Category
- Library
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
3.3 Fixed Single Point Approach3.3.1 Single Point Algorithm; 3.3.2 Statements Based on Single Point Algorithm; 3.3.3 Generalization of a Fixed Point to GLC; 3.4 Theoretical Limits to Preserve n-D Distances in 2-D: Johnson-Lindenstrauss Lemma; 3.5 Visual Representation of n-D Relations in GLC; 3.5.1
3.3 Fixed Single Point Approach3.3.1 Single Point Algorithm; 3.3.2 Statements Based on Single Point Algorithm; 3.3.3 Generalization of a Fixed Point to GLC; 3.4 Theoretical Limits to Preserve n-D Distances in 2-D: Johnson-Lindenstrauss Lemma; 3.5 Visual Representation of n-D Relations in GLC; 3.5.1
Focus on the commonalities concerning data analysis in computer science and in statistics Emphasis on both methods (statistical analysis and machine learning) and applications (marketing, finance, bioinformatics, musicology, psychology) Presentation of general methods and techniques that can be ap
<p>Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketi