| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNISALENTO991004361137907536 |
|
|
Autore |
Andrés, Juan |
|
|
Titolo |
Dell'origine, progressi e stato attuale di ogni letteratura / del padre Giovanni Andres della Compagnia di Gesù |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Napoli : Borel e Bompard, 1836-1838 |
|
|
|
|
|
|
|
Edizione |
[Nuova ed. conforme all'ultima di Roma con giunte e correzioni dell'autore e l'elogio storico del medesimo scritto da Angelo Antonio Scotti] |
|
|
|
|
|
|
|
Descrizione fisica |
|
|
|
|
|
|
Altri autori (Persone) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
Tomo 2. - 1836. - XVI, 375 p. |
Tomo 3.1. - 1836. - 193 p. |
Tomo 3.2. - 1836. - VIII, 314 p. |
Tomo 4. - 1837. - VII, 315 p. |
Tomo 5. - 1837. - VII, 334 p. |
Tomo 6. - 1837. - VIII, 395 p. |
Tomo 7. - 1838. - XVI, 295 p. |
Tomo 8. - 1838. - XII, 335 p. |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2. |
Record Nr. |
UNINA9910811879303321 |
|
|
Autore |
Pal Sankar K |
|
|
Titolo |
Foundations of soft case-based reasoning / / Sankar K. Pal, Simon C.K. Shiu |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Hoboken, N.J., : John Wiley & Sons, c2004 |
|
|
|
|
|
|
|
ISBN |
|
9786610367986 |
9781280367984 |
1280367989 |
9780470346686 |
047034668X |
9780471644668 |
0471644668 |
9780471644675 |
0471644676 |
|
|
|
|
|
|
|
|
Edizione |
[1st ed.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (298 p.) |
|
|
|
|
|
|
Collana |
|
Wiley series on intelligent systems |
|
|
|
|
|
|
Altri autori (Persone) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Soft computing |
Case-based reasoning |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
"A Wiley-Interscience publication." |
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references and index. |
|
|
|
|
|
|
Nota di contenuto |
|
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 |
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 CASE REPRESENTATION AND INDEXING; 2.1 Introduction; 2.2 Traditional Methods of Case Representation; 2.2.1 Relational Representation |
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 |
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 |
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 |
3.8.4 Experimental Results |
|
|
|
|
|
|
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. |
|
|
|
|
|
|
|
| |