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Author Awange, Joseph.
Title Hybrid Imaging and Visualization : Employing Machine Learning with Mathematica - Python / Joseph Awange, Bela Palancz, Lajos Volgyesi.
Imprint Cham : Springer, [2020]

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Author Awange, Joseph.
Subject Computer vision.
Alt Name Paláncz, Béla, 1944-
Völgyesi, Lajos.
Description 1 online resource (419 pages)
Note Description based upon print version of record.
3.5.2 Optimal Number of Clusters
Contents Intro; Preface; Acknowledgements; Contents; Introduction; 1 Computer Vision and Machine Learning; 2 Python and Mathematica; References; Chapter 1: Dimension Reduction; 1.1 Principal Component Analysis; Basic theory; 1.1.1 Principal Component; 1.1.2 Singular Value Decomposition; 1.1.3 Karhunen-Loeve Decomposition; 1.1.4 PCA and Total Least Square; 1.1.5 Image Compression; 1.1.6 Color Image Compression; 1.1.7 Image Compression in Python; 1.2 Independent Component Analysis; Basic theory; 1.2.1 Independent Component Analysis; 1.2.2 Image Compression via ICA; Mathematica; Python
1.3 Discrete Fourier TransformBasic theory; 1.3.1 Data Compression via DFT; 1.3.2 DFT Image Compression; 1.4 Discrete Wavelet Transform; Basic theory; 1.4.1 Concept of Discrete Wavelet Transform; 1.4.2 2D Discrete Wavelet Transform; 1.4.3 DWT Image Compression; 1.5 Radial Basis Function; Basic theory; 1.5.1 RBF Approximation; 1.5.2 RBF Image Compression; 1.6 AutoEncoding; Basic theory; 1.6.1 Concept of AutoEncoding; 1.6.2 Simple Example; 1.6.3 Compression of Image; 1.7 Fractal Compression; Basic theory; 1.7.1 Concept of Fractal Compression; 1.7.2 Illustrative Example; Mathematica
1.7.3 Image Compression with Python1.7.4 Accelerating Fractal Code Book Computation; 1.8 Comparison of Dimension Reduction Methods; 1.8.1 Measure of Image Quality; 1.8.2 Comparing Different Images; 1.8.3 Compression of Mandala; PCA method; Discrete Wavelet Transform; References; Chapter 2: Classification; 2.1 KNearest Neighbors Classification; Basic theory; 2.1.1 Small Data Set; Mathematica; Python; Mathematica; Python; 2.1.2 Vacant and Residential Lands; Vacant Land; Mathematica; Python; 2.2 Logistic Regression; Basic theory; 2.2.1 Iris Data Set; Python; Mathematica; 2.2.2 Digit Recognition
MathematicaPython; 2.3 Tree Based Methods; Basic theory; 2.3.1 Playing Tennis Today?; Mathematica; 2.3.2 Snowmen and Dice; Mathematica; Python; 2.4 Support Vector Classification; Basic theory; 2.4.1 Margin maximization; Mathematica; Python; 2.4.2 Feature Space Mapping; Mathematica; Python; 2.4.3 Learning Chess Board Fields; Mathematica; Python; 2.5 Naive Bayes Classifier; Basic theory; 2.5.1 Playing Tennis Today?; Mathematica; Python; 2.5.2 Zebra, Gorilla, Horse and Penguin; Mathematica; Python; 2.6 Comparison of Classification Methods; References; Chapter 3: Clustering; 3.1 KMeans Clustering
Basic theory3.1.1 Small Data Set; Python; Mathematica; 3.1.2 Clustering Images; Mathematica; 3.2 Hierarchical Clustering; Basic theory; 3.2.1 Dendrogram for Small Data Set; Python; 3.2.2 Image Segmentation; Mathematica; Python; 3.3 Density-Based Spatial Clustering of Applications with Noise; Basic theory; 3.3.1 Data Set Moons; Mathematica; Python; 3.3.2 Segmentation of MRI of Brain; 3.4 Spectral Clustering; Basic theory; 3.4.1 Nonlinear Data Set Moons; Mathematica; Python; 3.4.2 Image Coloring; 3.5 Comparison of Clustering Methods; 3.5.1 Measurement of Quality of Cluster Analysis
Bibliography Note Includes bibliographical references.
ISBN 9783030261535
OCLC # 1129181668
Additional Format Print version: Awange, Joseph Hybrid Imaging and Visualization : Employing Machine Learning with Mathematica - Python Cham : Springer,c2019 9783030261528.

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