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020 _a9789811510410
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024 7 _a10.1007/978-981-15-1041-0
_2doi
050 4 _aTA329-348
050 4 _aTA345-345.5
072 7 _aTBJ
_2bicssc
072 7 _aTEC009000
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072 7 _aTBJ
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082 0 4 _a620
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245 1 0 _aAlgorithms in Machine Learning Paradigms
_h[electronic resource] /
_cedited by Jyotsna Kumar Mandal, Somnath Mukhopadhyay, Paramartha Dutta, Kousik Dasgupta.
250 _a1st ed. 2020.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2020.
300 _aX, 195 p. 115 illus., 69 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v870
505 0 _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: 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.
520 _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. .
650 0 _aEngineering mathematics.
_93254
650 0 _aEngineering—Data processing.
_931556
650 0 _aMachine learning.
_91831
650 0 _aComputer vision.
_937901
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aSignal processing.
_94052
650 1 4 _aMathematical and Computational Engineering Applications.
_931559
650 2 4 _aMachine Learning.
_91831
650 2 4 _aComputer Vision.
_937902
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aSignal, Speech and Image Processing .
_931566
700 1 _aMandal, Jyotsna Kumar.
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_937903
700 1 _aMukhopadhyay, Somnath.
_eeditor.
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_937904
700 1 _aDutta, Paramartha.
_eeditor.
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_937905
700 1 _aDasgupta, Kousik.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
_937906
710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9789811510434
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v870
_937908
856 4 0 _uhttps://doi.org/10.1007/978-981-15-1041-0
912 _aZDB-2-ENG
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