04400nam 22007215 450 991037390160332120251113183335.0981-15-1041-510.1007/978-981-15-1041-0(CKB)4900000000505005(DE-He213)978-981-15-1041-0(MiAaPQ)EBC6114074(PPN)242845193(EXLCZ)99490000000050500520200103d2020 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierAlgorithms in Machine Learning Paradigms /edited by Jyotsna Kumar Mandal, Somnath Mukhopadhyay, Paramartha Dutta, Kousik Dasgupta1st ed. 2020.Singapore :Springer Nature Singapore :Imprint: Springer,2020.1 online resource (X, 195 p. 115 illus., 69 illus. in color.)Studies in Computational Intelligence,1860-9503 ;870981-15-1040-7 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.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. .Studies in Computational Intelligence,1860-9503 ;870Engineering mathematicsEngineeringData processingMachine learningComputer visionNatural language processing (Computer science)Signal processingMathematical and Computational Engineering ApplicationsMachine LearningComputer VisionNatural Language Processing (NLP)Signal, Speech and Image ProcessingEngineering mathematics.EngineeringData processing.Machine learning.Computer vision.Natural language processing (Computer science).Signal processing.Mathematical and Computational Engineering Applications.Machine Learning.Computer Vision.Natural Language Processing (NLP).Signal, Speech and Image Processing.006.31Mandal Jyotsna Kumaredthttp://id.loc.gov/vocabulary/relators/edtMukhopadhyay Somnath1983-edthttp://id.loc.gov/vocabulary/relators/edtDutta Paramarthaedthttp://id.loc.gov/vocabulary/relators/edtDasgupta Kousikedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910373901603321Algorithms in Machine Learning Paradigms2506760UNINA