LEADER 04400nam 22007215 450 001 9910373901603321 005 20251113183335.0 010 $a981-15-1041-5 024 7 $a10.1007/978-981-15-1041-0 035 $a(CKB)4900000000505005 035 $a(DE-He213)978-981-15-1041-0 035 $a(MiAaPQ)EBC6114074 035 $a(PPN)242845193 035 $a(EXLCZ)994900000000505005 100 $a20200103d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAlgorithms in Machine Learning Paradigms /$fedited by Jyotsna Kumar Mandal, Somnath Mukhopadhyay, Paramartha Dutta, Kousik Dasgupta 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (X, 195 p. 115 illus., 69 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v870 311 08$a981-15-1040-7 327 $aChapter 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. 330 $aThis book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence-based learning. . 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v870 606 $aEngineering mathematics 606 $aEngineering$xData processing 606 $aMachine learning 606 $aComputer vision 606 $aNatural language processing (Computer science) 606 $aSignal processing 606 $aMathematical and Computational Engineering Applications 606 $aMachine Learning 606 $aComputer Vision 606 $aNatural Language Processing (NLP) 606 $aSignal, Speech and Image Processing 615 0$aEngineering mathematics. 615 0$aEngineering$xData processing. 615 0$aMachine learning. 615 0$aComputer vision. 615 0$aNatural language processing (Computer science). 615 0$aSignal processing. 615 14$aMathematical and Computational Engineering Applications. 615 24$aMachine Learning. 615 24$aComputer Vision. 615 24$aNatural Language Processing (NLP). 615 24$aSignal, Speech and Image Processing. 676 $a006.31 702 $aMandal$b Jyotsna Kumar$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMukhopadhyay$b Somnath$f1983-$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDutta$b Paramartha$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDasgupta$b Kousik$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910373901603321 996 $aAlgorithms in Machine Learning Paradigms$92506760 997 $aUNINA