Algorithms in Machine Learning Paradigms  [electronic resource] / 
edited by Jyotsna Kumar Mandal, Somnath Mukhopadhyay, Paramartha Dutta, Kousik Dasgupta. 
 - 1st ed. 2020. 
 - X, 195 p. 115 illus., 69 illus. in color.  online resource. 
 - Studies in Computational Intelligence,  870  1860-9503 ; .
 - Studies in Computational Intelligence,  870 .
<br/><br/>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: A Combined 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. 
<br/><br/> 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. . 
<br/><br/><label>ISBN: </label>9789811510410 
<br/><br/><label>Standard No.: </label>10.1007/978-981-15-1041-0  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Engineering mathematics.<br/>Engineering—Data processing.<br/>Machine learning.<br/>Computer vision.<br/>Natural language processing (Computer science).<br/>Signal processing.<br/>Mathematical and Computational Engineering Applications.<br/>Machine Learning.<br/>Computer Vision.<br/>Natural Language Processing (NLP).<br/>Signal, Speech and Image Processing .
<br/><br/><label>LC Class. No.: </label>TA329-348 TA345-345.5 
<br/><br/><label>Dewey Class. No.: </label>620