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[null,"1.","Record Nr.",null,"UNISALENTO991002072339707536"]
[null,null,"Autore",null,"Giannattasio, Bianca Maria"]
[null,null,"Titolo",null,"Atti : 8. Giornata archeologica : mostre, musei, parchi archeologici : tre realtà a confronto : Genova, 1. dicembre 1995 / a cura di Bianca Maria Giannattasio"]
[null,null,"Pubbl/distr/stampa",null,"Genova : Università di Genova, Facoltà di lettere, 1996"]
[null,null,"Descrizione fisica",null,"159 p. : ill. ; 23 cm."]
[null,null,"Collana",null,"Pubblicazioni del D.AR.FI.CL.ET. ; nuova ser.  164"]
[null,null,"Disciplina",null,"937.0075"]
[null,null,"Soggetti",null,"Italia - Antichità - Congressi"]
[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,"UNINA9910811879303321"]
[null,null,"Autore",null,"Pal Sankar K"]
[null,null,"Titolo",null,"Foundations of soft case-based reasoning / / Sankar K. Pal, Simon C.K. Shiu"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, N.J., : John Wiley & Sons, c2004"]
[null,null,"ISBN",null,"9786610367986"]
[{"$nulls":4},"9781280367984"]
[{"$nulls":4},"1280367989"]
[{"$nulls":4},"9780470346686"]
[{"$nulls":4},"047034668X"]
[{"$nulls":4},"9780471644668"]
[{"$nulls":4},"0471644668"]
[{"$nulls":4},"9780471644675"]
[{"$nulls":4},"0471644676"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (298 p.)"]
[null,null,"Collana",null,"Wiley series on intelligent systems"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Soft computing"]
[{"$nulls":4},"Case-based reasoning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"A Wiley-Interscience publication.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"FOUNDATIONS OF SOFT CASE-BASED REASONING; CONTENTS; FOREWORD; PREFACE; ABOUT THE AUTHORS; 1 INTRODUCTION; 1.1 Background; 1.2 Components and Features of Case-Based Reasoning; 1.2.1 CBR System versus Rule-Based System; 1.2.2 CBR versus Human Reasoning; 1.2.3 CBR Life Cycle; 1.3 Guidelines for the Use of Case-Based Reasoning; 1.4 Advantages of Using Case-Based Reasoning; 1.5 Case Representation and Indexing; 1.5.1 Case Representation; 1.5.2 Case Indexing; 1.6 Case Retrieval; 1.7 Case Adaptation; 1.8 Case Learning and Case-Base Maintenance; 1.8.1 Learning in CBR Systems"]
[{"$nulls":4},"1.8.2 Case-Base Maintenance1.9 Example of Building a Case-Based Reasoning System; 1.9.1 Case Representation; 1.9.2 Case Indexing; 1.9.3 Case Retrieval; 1.9.4 Case Adaptation; 1.9.5 Case-Base Maintenance; 1.10 Case-Based Reasoning: Methodology or Technology?; 1.11 Soft Case-Based Reasoning; 1.11.1 Fuzzy Logic; 1.11.2 Neural Networks; 1.11.3 Genetic Algorithms; 1.11.4 Some CBR Tasks for Soft Computing Applications; 1.12 Summary; References; 2 "]
[{"$nulls":4},"CASE REPRESENTATION AND INDEXING; 2.1 Introduction; 2.2 Traditional Methods of Case Representation; 2.2.1 Relational Representation"]
[{"$nulls":4},"2.2.2 Object-Oriented Representation2.2.3 Predicate Representation; 2.2.4 Comparison of Case Representations; 2.3 Soft Computing Techniques for Case Representation; 2.3.1 Case Knowledge Representation Based on Fuzzy Sets; 2.3.2 Rough Sets and Determining Reducts; 2.3.3 Prototypical Case Generation Using Reducts with Fuzzy Representation; 2.4 Case Indexing; 2.4.1 Traditional Indexing Method; 2.4.2 Case Indexing Using a Bayesian Model; 2.4.3 Case Indexing Using a Prototype-Based Neural Network; 2.4.4 Case Indexing Using a Three-Layered Back Propagation Neural Network; 2.5 Summary; References"]
[{"$nulls":4},"3 CASE SELECTION AND RETRIEVAL3.1 Introduction; 3.2 Similarity Concept; 3.2.1 Weighted Euclidean Distance; 3.2.2 Hamming and Levenshtein Distances; 3.2.3 Cosine Coefficient for Text-Based Cases; 3.2.4 Other Similarity Measures; 3.2.5 k-Nearest Neighbor Principle; 3.3 Concept of Fuzzy Sets in Measuring Similarity; 3.3.1 Relevance of Fuzzy Similarity in Case Matching; 3.3.2 Computing Fuzzy Similarity Between Cases; 3.4 Fuzzy Classification and Clustering of Cases; 3.4.1 Weighted Intracluster and Intercluster Similarity; 3.4.2 Fuzzy ID3 Algorithm for Classification"]
[{"$nulls":4},"3.4.3 Fuzzy c-Means Algorithm for Clustering3.5 Case Feature Weighting; 3.5.1 Using Gradient-Descent Technique and Neural Networks; 3.5.2 Using Genetic Algorithms; 3.6 Case Selection and Retrieval Using Neural Networks; 3.6.1 Methodology; 3.6.2 Glass Identification; 3.7 Case Selection Using a Neuro-Fuzzy Model; 3.7.1 Selection of Cases and Class Representation; 3.7.2 Formulation of the Network; 3.8 Case Selection Using Rough-Self Organizing Map; 3.8.1 Pattern Indiscernibility and Fuzzy Discretization of Feature Space; 3.8.2 Methodology for Generation of Reducts; 3.8.3 Rough SOM"]
[{"$nulls":4},"3.8.4 Experimental Results"]
[null,null,null,"Sommario/riassunto","Provides a self-contained description of this important aspect of information processing and decision support technology.Presents basic definitions, principles, applications, and a detailed bibliography.Covers a range of real-world examples including control, data mining, and pattern recognition."]