Algorithms in Machine Learning Paradigms
Series: Studies in Computational Intelligence; 870;
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Product details:
- Edition number 1st ed. 2020
- Publisher Springer Nature Singapore
- Date of Publication 4 January 2021
- Number of Volumes 1 pieces, Book
- ISBN 9789811510434
- Binding Paperback
- See also 9789811510403
- No. of pages195 pages
- Size 235x155 mm
- Weight 454 g
- Language English
- Illustrations X, 195 p. 115 illus., 69 illus. in color. Illustrations, black & white 118
Categories
Long description:
This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence-based learning.
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Table of Contents:
Chapter 1. Development of Trapezoidal Hesitant-Intuitionistic Fuzzy Prioritized Operators based on Einstein Operations with their Application to Multi-Criteria Group Decision Making.- Chapter 2. Graph-based Information-Theoretic Approach for Unsupervised Feature Selection.- Chapter 3. Fact based Expert System for supplier selection with ERP data.- Chapter 4. Handling Seasonal Pattern and Prediction using Fuzzy Time Series Model.- Chapter 5. Automatic Classification of Fruits and Vegetables: A Texture-based Approach.- Chapter 6. Deep Learning based Early Sign Detection Model for Proliferative Diabetic Retinopathy in Neovascularization at the Disc.- Chapter 7. A Linear Regression Based Resource Utilization Prediction Policy For Live Migration in Cloud Computing.- Chapter 8. Tracking changing human emotions from facial image sequence by landmark triangulation: A incircle-circumcircle duo approach.- Chapter 9. Recognizing Human Emotions from Facial Images by Landmark Triangulation: ACombined Circumcenter-Incenter-Centroid Trio Feature Based Method.- Chapter 10. Stable neighbor nodes prediction with multivariate analysis in mobile ad hoc network using RNN model.- Chapter 11. A New Approach for Optimizing Initial Parameters of Lorenz Attractor and its application in PRNG.
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