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[null,"1.","Record Nr.",null,"UNISA990002159760203316"]
[null,null,"Autore",null,"FERRARESI, Franco"]
[null,null,"Titolo",null,"Studi sul potere locale : materiali per l'analisi delle strutture di potere nelle comunità locali con particolare riferimento agli studi classici americani / Franco Ferraresi"]
[null,null,"Pubbl/distr/stampa",null,"Milano : Giuffrè, 1971"]
[null,null,"Descrizione fisica",null,"138 p. ; 25 cm"]
[null,null,"Collana",null,"Quaderni ISAP , Saggi ; 11"]
[null,null,"Collocazione",null,"IG V 1 034"]
[{"$nulls":4},"XXIV.3. Coll. 29/ 11 (Collez UL 11)"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910455562503321"]
[null,null,"Autore",null,"Rokach Lior"]
[null,null,"Titolo",null,"Pattern classification using ensemble methods [[electronic resource] /] / Lior Rokach"]
[null,null,"Pubbl/distr/stampa",null,"Singapore ; ; Hackensack, NJ, : World Scientific, c2010"]
[null,null,"ISBN",null,"1-282-75785-7"]
[{"$nulls":4},"9786612757853"]
[{"$nulls":4},"981-4271-07-1"]
[null,null,"Descrizione fisica",null,"1 online resource (242 p.)"]
[null,null,"Collana",null,"Series in machine perception and artificial intelligence ; ; v. 75"]
[null,null,"Disciplina",null,"621.389/28"]
[null,null,"Soggetti",null,"Pattern recognition systems"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. 185-222) and index."]
[null,null,"Nota di contenuto",null,"Contents; Preface; 1. Introduction to Pattern Classification; 1.1 Pattern Classification; 1.2 Induction Algorithms; 1.3 Rule Induction; 1.4 Decision Trees; 1.5 Bayesian Methods; 1.5.1 Overview.; 1.5.2 Naıve Bayes; 1.5.2.1 The Basic Naıve Bayes Classifier; 1.5.2.2 Naıve Bayes Induction for Numeric Attributes; 1.5.2.3 Correction to the Probability Estimation; 1.5.2.4 Laplace Correction; 1.5.2.5 No Match; 1.5.3 Other Bayesian Methods; 1.6 Other Induction Methods; 1.6.1 Neural Networks; 1.6.2 Genetic Algorithms; 1.6.3 Instance-based Learning; 1.6.4 Support Vector Machines"]
[{"$nulls":4},"2. Introduction to Ensemble Learning 2.1 Back to the Roots; 2.2 The Wisdom of Crowds; 2.3 The Bagging Algorithm; 2.4 The Boosting Algorithm; 2.5 The Ada Boost Algorithm; 2.6 No Free Lunch Theorem and Ensemble Learning; 2.7 Bias-Variance Decomposition and Ensemble Learning; 2.8 Occam's Razor and Ensemble Learning; 2.9 Classifier Dependency; 2.9.1 Dependent Methods; 2.9.1.1 Model-guided Instance Selection; 2.9.1.2 Basic Boosting Algorithms; 2.9.1.3 Advanced Boosting Algorithms; 2.9.1.4 Incremental Batch Learning; 2.9.2 Independent Methods; 2.9.2.1 Bagging; 2.9.2.2 Wagging"]
[{"$nulls":4},"2.9.2.3 Random Forest and Random Subspace Projection 2.9.2.4 Non-Linear Boosting Projection (NLBP); 2.9.2.5 Cross-validated Committees; 2.9.2.6 Robust Boosting; 2.10 Ensemble Methods for Advanced "]
[{"$nulls":4},"Classification Tasks; 2.10.1 Cost-Sensitive Classification; 2.10.2 Ensemble for Learning Concept Drift; 2.10.3 Reject Driven Classification; 3. Ensemble Classification; 3.1 Fusions Methods; 3.1.1 Weighting Methods; 3.1.2 Majority Voting; 3.1.3 Performance Weighting; 3.1.4 Distribution Summation; 3.1.5 Bayesian Combination; 3.1.6 Dempster-Shafer; 3.1.7 Vogging; 3.1.8 Naıve Bayes"]
[{"$nulls":4},"3.1.9 Entropy Weighting 3.1.10 Density-based Weighting; 3.1.11 DEA Weighting Method; 3.1.12 Logarithmic Opinion Pool; 3.1.13 Order Statistics; 3.2 Selecting Classification; 3.2.1 Partitioning the Instance Space; 3.2.1.1 The K-Means Algorithm as a Decomposition Tool; 3.2.1.2 Determining the Number of Subsets; 3.2.1.3 The Basic K-Classifier Algorithm; 3.2.1.4 The Heterogeneity Detecting K-Classifier (HDK-Classifier); 3.2.1.5 Running-Time Complexity; 3.3 Mixture of Experts and Meta Learning; 3.3.1 Stacking; 3.3.2 Arbiter Trees; 3.3.3 Combiner Trees; 3.3.4 Grading; 3.3.5 Gating Network"]
[{"$nulls":4},"4. Ensemble Diversity 4.1 Overview; 4.2 Manipulating the Inducer; 4.2.1 Manipulation of the Inducer's Parameters; 4.2.2 Starting Point in Hypothesis Space; 4.2.3 Hypothesis Space Traversal; 4.3 Manipulating the Training Samples; 4.3.1 Resampling; 4.3.2 Creation; 4.3.3 Partitioning; 4.4 Manipulating the Target Attribute Representation; 4.4.1 Label Switching; 4.5 Partitioning the Search Space; 4.5.1 Divide and Conquer; 4.5.2 Feature Subset-based Ensemble Methods; 4.5.2.1 Random-based Strategy; 4.5.2.2 Reduct-based Strategy; 4.5.2.3 Collective-Performance-based Strategy"]
[{"$nulls":4},"4.5.2.4 Feature Set Partitioning"]
[null,null,null,"Sommario/riassunto","Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications.  The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions"]
[null,"3.","Record Nr.",null,"UNINA9910299248603321"]
[null,null,"Titolo",null,"Advances in Production Management Systems: Innovative Production Management Towards Sustainable Growth : IFIP WG 5.7 International Conference, APMS 2015, Tokyo, Japan, September 7-9, 2015, Proceedings, Part I / / edited by Shigeki Umeda, Masaru Nakano, Hajime Mizuyama, Nironori Hibino, Dimitris Kiritsis, Gregor von Cieminski"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015"]
[null,null,"ISBN",null,"3-319-22756-4"]
[null,null,"Edizione",null,"[1st ed. 2015.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XXX, 743 p. 245 illus.)"]
[null,null,"Collana",null,"IFIP Advances in Information and Communication Technology, , 1868-422X ; ; 459"]
[null,null,"Disciplina",null,"658.5"]
[null,null,"Soggetti",null,"Computer-aided engineering"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Business information services"]
[{"$nulls":4},"Computer-Aided Engineering (CAD, CAE) and Design"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"IT in Business"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Collaborative networks -- Globalization and production management -- Knowledge based production management -- Project management, engineering management, and quality management -- Sustainability and production management -- Co-creating sustainable business processes and ecosystems -- Open cloud computing architecture for smart manufacturing and cyber physical production systems -- The practitioner's view on \"innovative production management towards sustainable growth\" -- The role of additive manufacturing in value chain reconfiguration and sustainability -- Operations management in engineer-to-order manufacturing -- Lean production -- Sustainable system design for green products -- Cloud-based manufacturing -- Ontology-aided production - towards open and knowledge-driven planning and control -- Product-service lifecycle management: knowledge-driven innovation and social implications -- Service engineering."]
[null,null,null,"Sommario/riassunto","The two volumes IFIP AICT 459 and 460 constitute the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2015, held in Tokyo, Japan, in September 2015. The 163 revised full papers were carefully reviewed and selected from 185 submissions. They are organized in the following topical sections: collaborative networks; globalization and production management; knowledge based production management; project management, engineering management, and quality management; sustainability and production management; co-creating sustainable business processes and ecosystems; open cloud computing architecture for smart manufacturing and cyber physical production systems; the practitioner's view on \"innovative production management towards sustainable growth\"; the role of additive manufacturing in value chain reconfiguration and sustainability; operations management in engineer-to-order manufacturing; lean production; sustainable system design for green products; cloud-based manufacturing; ontology-aided production - towards open and knowledge-driven planning and control; product-service lifecycle management: knowledge-driven innovation and social implications; and service engineering."]