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Belief functions, theory and applications : 7th international conference, BELIEF 2022, Paris, France, October 26-28, 2022, proceedings / / edited by Sylvie Le Hégarat-Mascle, Isabelle Bloch, and Emanuel Aldea
| Belief functions, theory and applications : 7th international conference, BELIEF 2022, Paris, France, October 26-28, 2022, proceedings / / edited by Sylvie Le Hégarat-Mascle, Isabelle Bloch, and Emanuel Aldea |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (318 pages) |
| Disciplina | 658.403 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Decision making - Mathematical models
Decision making - Data processing |
| ISBN | 3-031-17801-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Evidential Clustering -- A Distributional Approach for Soft Clustering Comparison and Evaluation -- 1 Introduction -- 2 Background and Related Work -- 2.1 Background on Clustering -- 2.2 Clustering Comparison Measures -- 3 A General Framework for Soft Clustering Evaluation Measures -- 3.1 Distribution-Based Representation of Soft Clustering -- 3.2 Distributional Measures -- 3.3 Approximation Methods -- 4 Illustrative Experiment -- 5 Conclusion -- References -- Causal Transfer Evidential Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Transfer Evidential Clustering -- 2.2 Causal Feature Selection -- 3 Causal Transfer Evidential Clustering -- 4 Experiments -- 4.1 Synthetic Datasets -- 4.2 ALARM Network Dataset -- 5 Conclusion -- References -- A Variational Bayesian Clustering Approach to Acoustic Emission Interpretation Including Soft Labels -- 1 Introduction -- 2 Use of Soft Labels in a Variational Bayesian GMM -- 2.1 Directed Acyclic Graph -- 2.2 Learning Problem Under pl -- 2.3 Algorithm and Automatic Relevance Determination -- 3 First Results and First Conclusion -- 3.1 Data Set Description -- 3.2 The Priors -- 3.3 Sorting the Partitions -- 3.4 Results -- 4 Conclusion -- References -- Evidential Clustering by Competitive Agglomeration -- 1 Introduction -- 2 Background -- 2.1 Competitive Agglomeration (CA) -- 2.2 Basic Concepts of Belief Functions -- 3 Main Results -- 3.1 Basic Idea and Motivations -- 3.2 The Proposed Method -- 4 Experimental Evaluation -- 4.1 An Numerical Example: Four-Class Dataset -- 4.2 Compared with Other Clustering Methods -- 5 Conclusion -- References -- Imperfect Labels with Belief Functions for Active Learning -- 1 Introduction -- 2 Background -- 2.1 Reminder on Belief Functions -- 2.2 K-Nearest Neighbors -- 2.3 EK-NN -- 2.4 Active Learning.
3 Classification of Imperfectly Labeled Data with EK-NN and Active Learning -- 3.1 EK-NN for Imperfectly Labeled Data -- 3.2 Parameters Optimization and i-EKNN -- 3.3 Labeling with Uncertainty and Imprecision -- 4 Experiments -- 4.1 Different Approaches for Parameter -- 4.2 Experiment on Noised Real World Datasets -- 4.3 Experiment on Imperfectly Labeled Datasets -- 5 Conclusion -- References -- Machine Learning and Pattern Recognition -- An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers -- 1 Introduction -- 2 Epistemic Random Fuzzy Sets -- 2.1 General Framework -- 2.2 Gaussian Random Fuzzy Numbers -- 3 Neural Network Model -- 3.1 Propagation Equations -- 3.2 Loss Function -- 4 Experimental Results -- 4.1 Illustrative Example -- 4.2 Comparative Experiment -- 5 Conclusions -- References -- Ordinal Classification Using Single-Model Evidential Extreme Learning Machine -- 1 Introduction -- 2 Background -- 2.1 Dempster-Shafer Theory -- 2.2 Ordinal Extreme Learning Machine -- 3 Single-Model Multi-output Evidential Ordinal Extreme Learning Machine -- 3.1 Evidential Encoding Schemes -- 3.2 Construction of Evidential Ordinal ELM Model -- 4 Experiments -- 4.1 Artificial Dataset -- 4.2 UCI Datasets -- 5 Conclusion -- References -- Reliability-Based Imbalanced Data Classification with Dempster-Shafer Theory -- 1 Introduction -- 2 Reliability-Based Imbalanced Data Classification -- 2.1 Multiple Under-Sampling for Majority Class -- 2.2 Evaluate the Local Reliability for Classifiers Fusion -- 2.3 Employ Neighbors for Final Decision -- 3 Experiment Applications -- 3.1 Benchmark Datasets -- 3.2 Performance Evaluation -- 3.3 Influence of K and -- 3.4 Execution Time -- 4 Conclusion -- References -- Evidential Regression by Synthesizing Feature Selection and Parameters Learning -- 1 Introduction -- 2 Preliminaries. 2.1 Dempster-Shafer Theory -- 2.2 EVREG: Evidential Regression -- 3 Proposed Method -- 3.1 Construction of Evaluation Function -- 3.2 Feature Selection and Parameters Learning -- 4 Numerical Experiment -- 5 Conclusion -- References -- Algorithms and Evidential Operators -- Distributed EK-NN Classification -- 1 Introduction -- 2 Preliminaries -- 2.1 EK-NN: Evidential K-NN Classifier -- 2.2 Apache Spark -- 3 GE2K-NN: Global Exact EK-NN -- 4 Experiments -- 4.1 Performance Evaluation -- 4.2 Multi-node Experiments on TACC Frontera -- 5 Conclusions -- References -- On Improving a Group of Evidential Sources with Different Contextual Corrections -- 1 Introduction -- 2 Belief Functions: Notations and Concepts Used -- 2.1 Basic Concepts -- 2.2 Corrections -- 3 Learning a Group of Evidential Sources -- 4 Experiments -- 5 Conclusion -- References -- Measure of Information Content of Basic Belief Assignments -- 1 Introduction -- 2 Belief Functions -- 3 Generalized Entropy of a BBA -- 4 Information Content of a BBA -- 5 Information Gain and Information Loss -- 6 Conclusions -- References -- Belief Functions on Ordered Frames of Discernment -- 1 Introduction -- 2 Power Set of Ordered Elements -- 3 Combination of Belief Functions on Ordered Power Set -- 4 Distances on Belief Functions on Ordered Power Set -- 4.1 Distance Between Ordered Elements -- 4.2 Distance Between Belief Functions -- 5 Decision and Conflict on Ordered Elements -- 6 Belief Functions on Ordered Fuzzy Elements -- 7 Conclusion -- References -- On Modelling and Solving the Shortest Path Problem with Evidential Weights -- 1 Introduction -- 2 Preliminaries -- 2.1 Deterministic Shortest Path Problem -- 2.2 Belief Function Theory -- 3 Shortest Path Problem with Evidential Weights -- 3.1 Modelling -- 3.2 Solving -- 3.3 Sizes of Optweak and Optstr -- 4 Conclusion -- References. Data and Information Fusion -- Heterogeneous Image Fusion for Target Recognition Based on Evidence Reasoning -- 1 Introduction -- 2 Brief Recall of Evidence Reasoning -- 3 Heterogeneous Image Fusion for Target Recognition -- 3.1 Mutual Learning of the Networks for Heterogeneous Images -- 3.2 Weighted Fusion of Multiple Classification Results -- 4 Experiment -- 4.1 Datasets and Preprocessing -- 4.2 Experimental Environment and Parameter Settings -- 4.3 Effectiveness of the Mutual Learning of Heterogeneous Images -- 4.4 Results and Analysis -- 5 Conclusion -- References -- Cluster Decomposition of the Body of Evidence -- 1 Introduction -- 2 Basic Concepts of the Evidence Theory -- 3 Evidence Clustering -- 3.1 Restriction and Extension of the Mass Function -- 3.2 Statement of the Problem of Clustering the Body of Evidence Based on Conflict Optimization -- 3.3 Cluster Decomposition of Evidence Based on the Conflict Density Function -- 3.4 The k-Means Algorithm for the Body of Evidence -- 4 Evaluation of the Internal Conflict of the Body of Evidence Based on Its Clustering -- 5 Conclusion -- References -- Evidential Trustworthiness Estimation for Cooperative Perception -- 1 Introduction -- 2 Related Works -- 3 Problem Statement with Object Detectability -- 4 Evidential Trustworthiness Estimation -- 4.1 Coherency -- 4.2 Consistency -- 4.3 Confirmation Through Free Space and Objects -- 5 Results -- 5.1 Simulation Study -- 5.2 Experimental Results -- 6 Conclusion -- References -- An Intelligent System for Managing Uncertain Temporal Flood Events -- 1 Introduction -- 2 Preliminaries -- 2.1 Theory of Belief Functions -- 2.2 Allen's Interval Algebra -- 3 Temporal Representation and Reasoning Under Uncertainty -- 3.1 Modeling Uncertain Temporal Flood Events -- 3.2 Temporal Reasoning Under Uncertainty -- 4 Intelligent Query-Answering System. 4.1 System Architecture -- 4.2 Illustrative Examples -- 5 Conclusions and Future Work -- References -- Statistical Inference - Graphical Models -- A Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference -- 1 Introduction -- 2 Background -- 3 Valid Inference Under Partial Priors -- 3.1 Partial Priors -- 3.2 Validity and Its Consequences -- 3.3 How to Achieve (Strong) Validity -- 4 Practical IM Construction -- 4.1 Likelihood-Based Contour -- 4.2 Computation -- 5 Illustration -- 6 Conclusion -- References -- On Conditional Belief Functions in the Dempster-Shafer Theory -- 1 Introduction -- 2 Basics of D-S Theory of Belief Functions -- 3 Conditional Belief Functions -- 4 Summary and Conclusions -- References -- Valid Inferential Models Offer Performance and Probativeness Assurances -- 1 Introduction -- 2 Background -- 2.1 Two-Theory Problem -- 2.2 Inferential Models Overview -- 3 Two P's in the Same Pod -- 3.1 Performance -- 3.2 Probativeness -- 4 Illustrations -- 4.1 Normal Mean -- 4.2 Bivariate Normal Correlation -- 5 Conclusion -- References -- Links with Other Uncertainty Theories -- A Qualitative Counterpart of Belief Functions with Application to Uncertainty Propagation in Safety Cases -- 1 Introduction -- 2 From Belief Functions to Qualitative Capacities -- 3 Expert Elicitation Approach -- 4 Logical Inference for Qualitative Capacities -- 5 Application to Safety Cases -- 6 Application Example -- 7 Conclusion -- References -- The Extension of Dempster's Combination Rule Based on Generalized Credal Sets -- 1 Introduction -- 2 Basic Notions Concerning Monotone Measures and Belief Functions -- 3 Modelling Uncertainty by Belief Functions and Imprecise Probabilities -- 4 Contradictory Upper Previsions and Generalized Credal Sets -- 5 Updating Information Based on LG-Credal Sets -- 6 Generalized Credal Sets and Dempster's Rule. 7 Conclusion. |
| Record Nr. | UNINA-9910616364403321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Belief functions, theory and applications : 7th international conference, BELIEF 2022, Paris, France, October 26-28, 2022, proceedings / / edited by Sylvie Le Hégarat-Mascle, Isabelle Bloch, and Emanuel Aldea
| Belief functions, theory and applications : 7th international conference, BELIEF 2022, Paris, France, October 26-28, 2022, proceedings / / edited by Sylvie Le Hégarat-Mascle, Isabelle Bloch, and Emanuel Aldea |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (318 pages) |
| Disciplina | 658.403 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Decision making - Mathematical models
Decision making - Data processing |
| ISBN | 3-031-17801-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Evidential Clustering -- A Distributional Approach for Soft Clustering Comparison and Evaluation -- 1 Introduction -- 2 Background and Related Work -- 2.1 Background on Clustering -- 2.2 Clustering Comparison Measures -- 3 A General Framework for Soft Clustering Evaluation Measures -- 3.1 Distribution-Based Representation of Soft Clustering -- 3.2 Distributional Measures -- 3.3 Approximation Methods -- 4 Illustrative Experiment -- 5 Conclusion -- References -- Causal Transfer Evidential Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Transfer Evidential Clustering -- 2.2 Causal Feature Selection -- 3 Causal Transfer Evidential Clustering -- 4 Experiments -- 4.1 Synthetic Datasets -- 4.2 ALARM Network Dataset -- 5 Conclusion -- References -- A Variational Bayesian Clustering Approach to Acoustic Emission Interpretation Including Soft Labels -- 1 Introduction -- 2 Use of Soft Labels in a Variational Bayesian GMM -- 2.1 Directed Acyclic Graph -- 2.2 Learning Problem Under pl -- 2.3 Algorithm and Automatic Relevance Determination -- 3 First Results and First Conclusion -- 3.1 Data Set Description -- 3.2 The Priors -- 3.3 Sorting the Partitions -- 3.4 Results -- 4 Conclusion -- References -- Evidential Clustering by Competitive Agglomeration -- 1 Introduction -- 2 Background -- 2.1 Competitive Agglomeration (CA) -- 2.2 Basic Concepts of Belief Functions -- 3 Main Results -- 3.1 Basic Idea and Motivations -- 3.2 The Proposed Method -- 4 Experimental Evaluation -- 4.1 An Numerical Example: Four-Class Dataset -- 4.2 Compared with Other Clustering Methods -- 5 Conclusion -- References -- Imperfect Labels with Belief Functions for Active Learning -- 1 Introduction -- 2 Background -- 2.1 Reminder on Belief Functions -- 2.2 K-Nearest Neighbors -- 2.3 EK-NN -- 2.4 Active Learning.
3 Classification of Imperfectly Labeled Data with EK-NN and Active Learning -- 3.1 EK-NN for Imperfectly Labeled Data -- 3.2 Parameters Optimization and i-EKNN -- 3.3 Labeling with Uncertainty and Imprecision -- 4 Experiments -- 4.1 Different Approaches for Parameter -- 4.2 Experiment on Noised Real World Datasets -- 4.3 Experiment on Imperfectly Labeled Datasets -- 5 Conclusion -- References -- Machine Learning and Pattern Recognition -- An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers -- 1 Introduction -- 2 Epistemic Random Fuzzy Sets -- 2.1 General Framework -- 2.2 Gaussian Random Fuzzy Numbers -- 3 Neural Network Model -- 3.1 Propagation Equations -- 3.2 Loss Function -- 4 Experimental Results -- 4.1 Illustrative Example -- 4.2 Comparative Experiment -- 5 Conclusions -- References -- Ordinal Classification Using Single-Model Evidential Extreme Learning Machine -- 1 Introduction -- 2 Background -- 2.1 Dempster-Shafer Theory -- 2.2 Ordinal Extreme Learning Machine -- 3 Single-Model Multi-output Evidential Ordinal Extreme Learning Machine -- 3.1 Evidential Encoding Schemes -- 3.2 Construction of Evidential Ordinal ELM Model -- 4 Experiments -- 4.1 Artificial Dataset -- 4.2 UCI Datasets -- 5 Conclusion -- References -- Reliability-Based Imbalanced Data Classification with Dempster-Shafer Theory -- 1 Introduction -- 2 Reliability-Based Imbalanced Data Classification -- 2.1 Multiple Under-Sampling for Majority Class -- 2.2 Evaluate the Local Reliability for Classifiers Fusion -- 2.3 Employ Neighbors for Final Decision -- 3 Experiment Applications -- 3.1 Benchmark Datasets -- 3.2 Performance Evaluation -- 3.3 Influence of K and -- 3.4 Execution Time -- 4 Conclusion -- References -- Evidential Regression by Synthesizing Feature Selection and Parameters Learning -- 1 Introduction -- 2 Preliminaries. 2.1 Dempster-Shafer Theory -- 2.2 EVREG: Evidential Regression -- 3 Proposed Method -- 3.1 Construction of Evaluation Function -- 3.2 Feature Selection and Parameters Learning -- 4 Numerical Experiment -- 5 Conclusion -- References -- Algorithms and Evidential Operators -- Distributed EK-NN Classification -- 1 Introduction -- 2 Preliminaries -- 2.1 EK-NN: Evidential K-NN Classifier -- 2.2 Apache Spark -- 3 GE2K-NN: Global Exact EK-NN -- 4 Experiments -- 4.1 Performance Evaluation -- 4.2 Multi-node Experiments on TACC Frontera -- 5 Conclusions -- References -- On Improving a Group of Evidential Sources with Different Contextual Corrections -- 1 Introduction -- 2 Belief Functions: Notations and Concepts Used -- 2.1 Basic Concepts -- 2.2 Corrections -- 3 Learning a Group of Evidential Sources -- 4 Experiments -- 5 Conclusion -- References -- Measure of Information Content of Basic Belief Assignments -- 1 Introduction -- 2 Belief Functions -- 3 Generalized Entropy of a BBA -- 4 Information Content of a BBA -- 5 Information Gain and Information Loss -- 6 Conclusions -- References -- Belief Functions on Ordered Frames of Discernment -- 1 Introduction -- 2 Power Set of Ordered Elements -- 3 Combination of Belief Functions on Ordered Power Set -- 4 Distances on Belief Functions on Ordered Power Set -- 4.1 Distance Between Ordered Elements -- 4.2 Distance Between Belief Functions -- 5 Decision and Conflict on Ordered Elements -- 6 Belief Functions on Ordered Fuzzy Elements -- 7 Conclusion -- References -- On Modelling and Solving the Shortest Path Problem with Evidential Weights -- 1 Introduction -- 2 Preliminaries -- 2.1 Deterministic Shortest Path Problem -- 2.2 Belief Function Theory -- 3 Shortest Path Problem with Evidential Weights -- 3.1 Modelling -- 3.2 Solving -- 3.3 Sizes of Optweak and Optstr -- 4 Conclusion -- References. Data and Information Fusion -- Heterogeneous Image Fusion for Target Recognition Based on Evidence Reasoning -- 1 Introduction -- 2 Brief Recall of Evidence Reasoning -- 3 Heterogeneous Image Fusion for Target Recognition -- 3.1 Mutual Learning of the Networks for Heterogeneous Images -- 3.2 Weighted Fusion of Multiple Classification Results -- 4 Experiment -- 4.1 Datasets and Preprocessing -- 4.2 Experimental Environment and Parameter Settings -- 4.3 Effectiveness of the Mutual Learning of Heterogeneous Images -- 4.4 Results and Analysis -- 5 Conclusion -- References -- Cluster Decomposition of the Body of Evidence -- 1 Introduction -- 2 Basic Concepts of the Evidence Theory -- 3 Evidence Clustering -- 3.1 Restriction and Extension of the Mass Function -- 3.2 Statement of the Problem of Clustering the Body of Evidence Based on Conflict Optimization -- 3.3 Cluster Decomposition of Evidence Based on the Conflict Density Function -- 3.4 The k-Means Algorithm for the Body of Evidence -- 4 Evaluation of the Internal Conflict of the Body of Evidence Based on Its Clustering -- 5 Conclusion -- References -- Evidential Trustworthiness Estimation for Cooperative Perception -- 1 Introduction -- 2 Related Works -- 3 Problem Statement with Object Detectability -- 4 Evidential Trustworthiness Estimation -- 4.1 Coherency -- 4.2 Consistency -- 4.3 Confirmation Through Free Space and Objects -- 5 Results -- 5.1 Simulation Study -- 5.2 Experimental Results -- 6 Conclusion -- References -- An Intelligent System for Managing Uncertain Temporal Flood Events -- 1 Introduction -- 2 Preliminaries -- 2.1 Theory of Belief Functions -- 2.2 Allen's Interval Algebra -- 3 Temporal Representation and Reasoning Under Uncertainty -- 3.1 Modeling Uncertain Temporal Flood Events -- 3.2 Temporal Reasoning Under Uncertainty -- 4 Intelligent Query-Answering System. 4.1 System Architecture -- 4.2 Illustrative Examples -- 5 Conclusions and Future Work -- References -- Statistical Inference - Graphical Models -- A Practical Strategy for Valid Partial Prior-Dependent Possibilistic Inference -- 1 Introduction -- 2 Background -- 3 Valid Inference Under Partial Priors -- 3.1 Partial Priors -- 3.2 Validity and Its Consequences -- 3.3 How to Achieve (Strong) Validity -- 4 Practical IM Construction -- 4.1 Likelihood-Based Contour -- 4.2 Computation -- 5 Illustration -- 6 Conclusion -- References -- On Conditional Belief Functions in the Dempster-Shafer Theory -- 1 Introduction -- 2 Basics of D-S Theory of Belief Functions -- 3 Conditional Belief Functions -- 4 Summary and Conclusions -- References -- Valid Inferential Models Offer Performance and Probativeness Assurances -- 1 Introduction -- 2 Background -- 2.1 Two-Theory Problem -- 2.2 Inferential Models Overview -- 3 Two P's in the Same Pod -- 3.1 Performance -- 3.2 Probativeness -- 4 Illustrations -- 4.1 Normal Mean -- 4.2 Bivariate Normal Correlation -- 5 Conclusion -- References -- Links with Other Uncertainty Theories -- A Qualitative Counterpart of Belief Functions with Application to Uncertainty Propagation in Safety Cases -- 1 Introduction -- 2 From Belief Functions to Qualitative Capacities -- 3 Expert Elicitation Approach -- 4 Logical Inference for Qualitative Capacities -- 5 Application to Safety Cases -- 6 Application Example -- 7 Conclusion -- References -- The Extension of Dempster's Combination Rule Based on Generalized Credal Sets -- 1 Introduction -- 2 Basic Notions Concerning Monotone Measures and Belief Functions -- 3 Modelling Uncertainty by Belief Functions and Imprecise Probabilities -- 4 Contradictory Upper Previsions and Generalized Credal Sets -- 5 Updating Information Based on LG-Credal Sets -- 6 Generalized Credal Sets and Dempster's Rule. 7 Conclusion. |
| Record Nr. | UNISA-996490357703316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Fuzzy Logic and Applications [[electronic resource] ] : 6th International Workshop, WILF 2005, Crema, Italy, September 15-17, 2005, Revised Selected Papers / / edited by Isabelle Bloch, Alfredo Petrosino, Andrea G.B. Tettamanzi
| Fuzzy Logic and Applications [[electronic resource] ] : 6th International Workshop, WILF 2005, Crema, Italy, September 15-17, 2005, Revised Selected Papers / / edited by Isabelle Bloch, Alfredo Petrosino, Andrea G.B. Tettamanzi |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 |
| Descrizione fisica | 1 online resource (XIV, 438 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Mathematical logic Computers Information storage and retrieval Database management Optical data processing Artificial Intelligence Mathematical Logic and Formal Languages Computation by Abstract Devices Information Storage and Retrieval Database Management Image Processing and Computer Vision |
| ISBN | 3-540-32530-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- A Bipolar Possibilistic Representation of Knowledge and Preferences and Its Applications -- Statistical Distribution of Chemical Fingerprints -- Fuzzy Transforms and Their Applications to Image Compression -- Neuro-fuzzy Systems -- Development of Neuro-fuzzy System for Image Mining -- Reinforcement Distribution in Continuous State Action Space Fuzzy Q–Learning: A Novel Approach -- Fuzzy Logic and Possibility Theory -- A Possibilistic Approach to Combinatorial Optimization Problems on Fuzzy-Valued Matroids -- Possibilistic Planning Using Description Logics: A First Step -- Multi-lattices as a Basis for Generalized Fuzzy Logic Programming -- A Method for Characterizing Tractable Subsets of Qualitative Fuzzy Temporal Algebrae -- Reasoning and Quantification in Fuzzy Description Logics -- Programming with Fuzzy Logic and Mathematical Functions -- Efficient Methods for Computing Optimality Degrees of Elements in Fuzzy Weighted Matroids -- Imprecise Temporal Interval Relations -- A Many Valued Representation and Propagation of Trust and Distrust -- Pattern Recognition -- SVM Classification of Neonatal Facial Images of Pain -- Performance Evaluation of a Hand Gesture Recognition System Using Fuzzy Algorithm and Neural Network for Post PC Platform -- Implementation and Performance Evaluation of Glove-Based HCI Methods: Gesture Recognition Systems Using Fuzzy Algorithm and Neural Network for the Wearable PC -- A Hybrid Warping Method Approach to Speaker Warping Adaptation -- Evolutionary Algorithms -- Genetic Programming for Inductive Inference of Chaotic Series -- Evaluation of Particle Swarm Optimization Effectiveness in Classification -- Identification of Takagi-Sugeno Fuzzy Systems Based on Multi-objective Genetic Algorithms -- Genetic Programming and Neural Networks Feedback Linearization for Modeling and Controlling Complex Pharmacogenomic Systems -- OR/AND Neurons for Fuzzy Set Connectives Using Ordinal Sums and Genetic Algorithms -- Control -- Intelligent Track Analysis on Navy Platforms Using Soft Computing -- Software Implementation of Fuzzy Controller with Conditionally Firing Rules, and Experimental Comparisons -- Special Session: CIBB -- Adaptive Feature Selection for Classification of Microscope Images -- Genetic Algorithm Against Cancer -- Unsupervised Gene Selection and Clustering Using Simulated Annealing -- SpecDB: A Database for Storing and Managing Mass Spectrometry Proteomics Data -- NEC for Gene Expression Analysis -- Active Learning with Wavelets for Microarray Data -- Semi-supervised Fuzzy c-Means Clustering of Biological Data -- Comparison of Gene Identification Based on Artificial Neural Network Pre-processing with k-Means Cluster and Principal Component Analysis -- Biological Specifications for a Synthetic Gene Expression Data Generation Model -- Semisupervised Profiling of Gene Expressions and Clinical Data -- Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data -- Learning Bayesian Classifiers from Gene-Expression MicroArray Data -- Special Session: SCIP -- On the Evaluation of Images Complexity: A Fuzzy Approach -- 3D Brain Tumor Segmentation Using Fuzzy Classification and Deformable Models -- A Hybrid Architecture for the Sensorimotor Exploration of Spatial Scenes -- KANSEI-Based Image Retrieval Associated with Color -- Mass Detection in Mammograms Using Gabor Filters and Fuzzy Clustering -- MRF Model-Based Approach for Image Segmentation Using a Chaotic MultiAgent System -- Duality vs Adjunction and General Form for Fuzzy Mathematical Morphology -- A Fuzzy Mathematical Morphology Approach to Multiseeded Image Segmentation -- Neuro-fuzzy Analysis of Document Images by the KERNEL System -- Knowledge Management -- Intelligent Knowledge Capsule Design for Associative Priming Knowledge Extraction -- A Flexible Intelligent Associative Knowledge Structure of Reticular Activating System: Positive/Negative Masking -- Selective Immunity-Based Model Considering Filtering Information by Automatic Generated Positive/Negative Cells -- Exploring the Way for Meta-learning with the Mindful System -- Miscellaneous Applications -- Using Fuzzy Logic to Generate the Mesh for the Finite Element Method -- Unidirectional Two Dimensional Systolic Array for Multiplication in GF(2 m ) Using LSB First Algorithm -- Efficient Linear Array for Multiplication over NIST Recommended Binary Fields. |
| Record Nr. | UNISA-996466157003316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Fuzzy logic and applications : 6th international workshop, WILF 2005, Crema, Italy, September 15-17, 2005 : revised selected papers / / Isabelle Bloch, Alfredo Petrosino, Andrea G.B. Tettamanzi (eds.)
| Fuzzy logic and applications : 6th international workshop, WILF 2005, Crema, Italy, September 15-17, 2005 : revised selected papers / / Isabelle Bloch, Alfredo Petrosino, Andrea G.B. Tettamanzi (eds.) |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin ; ; New York, : Springer, c2006 |
| Descrizione fisica | 1 online resource (XIV, 438 p.) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
BlochIsabelle
PetrosinoAlfredo TettamanziAndrea |
| Collana | Lecture notes in computer science. Lecture notes in artificial intelligence |
| Soggetto topico |
Soft computing
Fuzzy systems Fuzzy logic |
| ISBN | 3-540-32530-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- A Bipolar Possibilistic Representation of Knowledge and Preferences and Its Applications -- Statistical Distribution of Chemical Fingerprints -- Fuzzy Transforms and Their Applications to Image Compression -- Neuro-fuzzy Systems -- Development of Neuro-fuzzy System for Image Mining -- Reinforcement Distribution in Continuous State Action Space Fuzzy Q–Learning: A Novel Approach -- Fuzzy Logic and Possibility Theory -- A Possibilistic Approach to Combinatorial Optimization Problems on Fuzzy-Valued Matroids -- Possibilistic Planning Using Description Logics: A First Step -- Multi-lattices as a Basis for Generalized Fuzzy Logic Programming -- A Method for Characterizing Tractable Subsets of Qualitative Fuzzy Temporal Algebrae -- Reasoning and Quantification in Fuzzy Description Logics -- Programming with Fuzzy Logic and Mathematical Functions -- Efficient Methods for Computing Optimality Degrees of Elements in Fuzzy Weighted Matroids -- Imprecise Temporal Interval Relations -- A Many Valued Representation and Propagation of Trust and Distrust -- Pattern Recognition -- SVM Classification of Neonatal Facial Images of Pain -- Performance Evaluation of a Hand Gesture Recognition System Using Fuzzy Algorithm and Neural Network for Post PC Platform -- Implementation and Performance Evaluation of Glove-Based HCI Methods: Gesture Recognition Systems Using Fuzzy Algorithm and Neural Network for the Wearable PC -- A Hybrid Warping Method Approach to Speaker Warping Adaptation -- Evolutionary Algorithms -- Genetic Programming for Inductive Inference of Chaotic Series -- Evaluation of Particle Swarm Optimization Effectiveness in Classification -- Identification of Takagi-Sugeno Fuzzy Systems Based on Multi-objective Genetic Algorithms -- Genetic Programming and Neural Networks Feedback Linearization for Modeling and Controlling Complex Pharmacogenomic Systems -- OR/AND Neurons for Fuzzy Set Connectives Using Ordinal Sums and Genetic Algorithms -- Control -- Intelligent Track Analysis on Navy Platforms Using Soft Computing -- Software Implementation of Fuzzy Controller with Conditionally Firing Rules, and Experimental Comparisons -- Special Session: CIBB -- Adaptive Feature Selection for Classification of Microscope Images -- Genetic Algorithm Against Cancer -- Unsupervised Gene Selection and Clustering Using Simulated Annealing -- SpecDB: A Database for Storing and Managing Mass Spectrometry Proteomics Data -- NEC for Gene Expression Analysis -- Active Learning with Wavelets for Microarray Data -- Semi-supervised Fuzzy c-Means Clustering of Biological Data -- Comparison of Gene Identification Based on Artificial Neural Network Pre-processing with k-Means Cluster and Principal Component Analysis -- Biological Specifications for a Synthetic Gene Expression Data Generation Model -- Semisupervised Profiling of Gene Expressions and Clinical Data -- Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data -- Learning Bayesian Classifiers from Gene-Expression MicroArray Data -- Special Session: SCIP -- On the Evaluation of Images Complexity: A Fuzzy Approach -- 3D Brain Tumor Segmentation Using Fuzzy Classification and Deformable Models -- A Hybrid Architecture for the Sensorimotor Exploration of Spatial Scenes -- KANSEI-Based Image Retrieval Associated with Color -- Mass Detection in Mammograms Using Gabor Filters and Fuzzy Clustering -- MRF Model-Based Approach for Image Segmentation Using a Chaotic MultiAgent System -- Duality vs Adjunction and General Form for Fuzzy Mathematical Morphology -- A Fuzzy Mathematical Morphology Approach to Multiseeded Image Segmentation -- Neuro-fuzzy Analysis of Document Images by the KERNEL System -- Knowledge Management -- Intelligent Knowledge Capsule Design for Associative Priming Knowledge Extraction -- A Flexible Intelligent Associative Knowledge Structure of Reticular Activating System: Positive/Negative Masking -- Selective Immunity-Based Model Considering Filtering Information by Automatic Generated Positive/Negative Cells -- Exploring the Way for Meta-learning with the Mindful System -- Miscellaneous Applications -- Using Fuzzy Logic to Generate the Mesh for the Finite Element Method -- Unidirectional Two Dimensional Systolic Array for Multiplication in GF(2 m ) Using LSB First Algorithm -- Efficient Linear Array for Multiplication over NIST Recommended Binary Fields. |
| Altri titoli varianti | WILF 2005 |
| Record Nr. | UNINA-9910484310403321 |
| Berlin ; ; New York, : Springer, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Information fusion in signal and image processing [[electronic resource] ] : major probabilistic and non-probabilistic numerical approaches / / edited by Isabelle Bloch
| Information fusion in signal and image processing [[electronic resource] ] : major probabilistic and non-probabilistic numerical approaches / / edited by Isabelle Bloch |
| Autore | Bloch Isabelle |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (297 p.) |
| Disciplina |
621.382/2
621.3822 |
| Altri autori (Persone) | BlochIsabelle |
| Collana | ISTE |
| Soggetto topico |
Signal processing
Image processing |
| ISBN |
1-282-16497-X
9786612164972 0-470-61107-3 0-470-39365-3 |
| Classificazione | ZN 6025 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Information Fusion in Signal and Image Processing; Table of Contents; Preface; Chapter 1. Definitions; 1.1. Introduction; 1.2. Choosing a definition; 1.3. General characteristics of the data; 1.4. Numerical/symbolic; 1.4.1. Data and information; 1.4.2. Processes; 1.4.3. Representations; 1.5. Fusion systems; 1.6. Fusion in signal and image processing and fusion in other fields; 1.7. Bibliography; Chapter 2. Fusion in Signal Processing; 2.1. Introduction; 2.2. Objectives of fusion in signal processing; 2.2.1. Estimation and calculation of a law a posteriori
2.2.2. Discriminating between several hypotheses and identifying2.2.3. Controlling and supervising a data fusion chain; 2.3. Problems and specificities of fusion in signal processing; 2.3.1. Dynamic control; 2.3.2. Quality of the information; 2.3.3. Representativeness and accuracy of learning and a priori information; 2.4. Bibliography; Chapter 3. Fusion in Image Processing; 3.1. Objectives of fusion in image processing; 3.2. Fusion situations; 3.3. Data characteristics in image fusion; 3.4. Constraints; 3.5. Numerical and symbolic aspects in image fusion; 3.6. Bibliography Chapter 4. Fusion in Robotics4.1. The necessity for fusion in robotics; 4.2. Specific features of fusion in robotics; 4.2.1. Constraints on the perception system; 4.2.2. Proprioceptive and exteroceptive sensors; 4.2.3. Interaction with the operator and symbolic interpretation; 4.2.4. Time constraints; 4.3. Characteristics of the data in robotics; 4.3.1. Calibrating and changing the frame of reference; 4.3.2. Types and levels of representation of the environment; 4.4. Data fusion mechanisms; 4.5. Bibliography; Chapter 5. Information and Knowledge Representation in Fusion Problems 5.1. Introduction5.2. Processing information in fusion; 5.3. Numerical representations of imperfect knowledge; 5.4. Symbolic representation of imperfect knowledge; 5.5. Knowledge-based systems; 5.6. Reasoning modes and inference; 5.7. Bibliography; Chapter 6. Probabilistic and Statistical Methods; 6.1. Introduction and general concepts; 6.2. Information measurements; 6.3. Modeling and estimation; 6.4. Combination in a Bayesian framework; 6.5. Combination as an estimation problem; 6.6. Decision; 6.7. Other methods in detection; 6.8. An example of Bayesian fusion in satellite imagery 6.9. Probabilistic fusion methods applied to target motion analysis6.9.1. General presentation; 6.9.2. Multi-platform target motion analysis; 6.9.3. Target motion analysis by fusion of active and passive measurements; 6.9.4. Detection of a moving target in a network of sensors; 6.10. Discussion; 6.11. Bibliography; Chapter 7. Belief Function Theory; 7.1. General concept and philosophy of the theory; 7.2. Modeling; 7.3. Estimation of mass functions; 7.3.1. Modification of probabilistic models; 7.3.2. Modification of distance models 7.3.3. A priori information on composite focal elements (disjunctions) |
| Record Nr. | UNINA-9910139517903321 |
Bloch Isabelle
|
||
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Information fusion in signal and image processing [[electronic resource] ] : major probabilistic and non-probabilistic numerical approaches / / edited by Isabelle Bloch
| Information fusion in signal and image processing [[electronic resource] ] : major probabilistic and non-probabilistic numerical approaches / / edited by Isabelle Bloch |
| Autore | Bloch Isabelle |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (297 p.) |
| Disciplina |
621.382/2
621.3822 |
| Altri autori (Persone) | BlochIsabelle |
| Collana | ISTE |
| Soggetto topico |
Signal processing
Image processing |
| ISBN |
1-282-16497-X
9786612164972 0-470-61107-3 0-470-39365-3 |
| Classificazione | ZN 6025 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Information Fusion in Signal and Image Processing; Table of Contents; Preface; Chapter 1. Definitions; 1.1. Introduction; 1.2. Choosing a definition; 1.3. General characteristics of the data; 1.4. Numerical/symbolic; 1.4.1. Data and information; 1.4.2. Processes; 1.4.3. Representations; 1.5. Fusion systems; 1.6. Fusion in signal and image processing and fusion in other fields; 1.7. Bibliography; Chapter 2. Fusion in Signal Processing; 2.1. Introduction; 2.2. Objectives of fusion in signal processing; 2.2.1. Estimation and calculation of a law a posteriori
2.2.2. Discriminating between several hypotheses and identifying2.2.3. Controlling and supervising a data fusion chain; 2.3. Problems and specificities of fusion in signal processing; 2.3.1. Dynamic control; 2.3.2. Quality of the information; 2.3.3. Representativeness and accuracy of learning and a priori information; 2.4. Bibliography; Chapter 3. Fusion in Image Processing; 3.1. Objectives of fusion in image processing; 3.2. Fusion situations; 3.3. Data characteristics in image fusion; 3.4. Constraints; 3.5. Numerical and symbolic aspects in image fusion; 3.6. Bibliography Chapter 4. Fusion in Robotics4.1. The necessity for fusion in robotics; 4.2. Specific features of fusion in robotics; 4.2.1. Constraints on the perception system; 4.2.2. Proprioceptive and exteroceptive sensors; 4.2.3. Interaction with the operator and symbolic interpretation; 4.2.4. Time constraints; 4.3. Characteristics of the data in robotics; 4.3.1. Calibrating and changing the frame of reference; 4.3.2. Types and levels of representation of the environment; 4.4. Data fusion mechanisms; 4.5. Bibliography; Chapter 5. Information and Knowledge Representation in Fusion Problems 5.1. Introduction5.2. Processing information in fusion; 5.3. Numerical representations of imperfect knowledge; 5.4. Symbolic representation of imperfect knowledge; 5.5. Knowledge-based systems; 5.6. Reasoning modes and inference; 5.7. Bibliography; Chapter 6. Probabilistic and Statistical Methods; 6.1. Introduction and general concepts; 6.2. Information measurements; 6.3. Modeling and estimation; 6.4. Combination in a Bayesian framework; 6.5. Combination as an estimation problem; 6.6. Decision; 6.7. Other methods in detection; 6.8. An example of Bayesian fusion in satellite imagery 6.9. Probabilistic fusion methods applied to target motion analysis6.9.1. General presentation; 6.9.2. Multi-platform target motion analysis; 6.9.3. Target motion analysis by fusion of active and passive measurements; 6.9.4. Detection of a moving target in a network of sensors; 6.10. Discussion; 6.11. Bibliography; Chapter 7. Belief Function Theory; 7.1. General concept and philosophy of the theory; 7.2. Modeling; 7.3. Estimation of mass functions; 7.3.1. Modification of probabilistic models; 7.3.2. Modification of distance models 7.3.3. A priori information on composite focal elements (disjunctions) |
| Record Nr. | UNINA-9910830598303321 |
|
Bloch Isabelle
|
||
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Information fusion in signal and image processing : major probabilistic and non-probabilistic numerical approaches / / edited by Isabelle Bloch
| Information fusion in signal and image processing : major probabilistic and non-probabilistic numerical approaches / / edited by Isabelle Bloch |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (297 p.) |
| Disciplina | 621.382/2 |
| Altri autori (Persone) | BlochIsabelle |
| Collana | ISTE |
| Soggetto topico |
Signal processing
Image processing |
| ISBN |
9786612164972
9781282164970 128216497X 9780470611074 0470611073 9780470393659 0470393653 |
| Classificazione | ZN 6025 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Information Fusion in Signal and Image Processing; Table of Contents; Preface; Chapter 1. Definitions; 1.1. Introduction; 1.2. Choosing a definition; 1.3. General characteristics of the data; 1.4. Numerical/symbolic; 1.4.1. Data and information; 1.4.2. Processes; 1.4.3. Representations; 1.5. Fusion systems; 1.6. Fusion in signal and image processing and fusion in other fields; 1.7. Bibliography; Chapter 2. Fusion in Signal Processing; 2.1. Introduction; 2.2. Objectives of fusion in signal processing; 2.2.1. Estimation and calculation of a law a posteriori
2.2.2. Discriminating between several hypotheses and identifying2.2.3. Controlling and supervising a data fusion chain; 2.3. Problems and specificities of fusion in signal processing; 2.3.1. Dynamic control; 2.3.2. Quality of the information; 2.3.3. Representativeness and accuracy of learning and a priori information; 2.4. Bibliography; Chapter 3. Fusion in Image Processing; 3.1. Objectives of fusion in image processing; 3.2. Fusion situations; 3.3. Data characteristics in image fusion; 3.4. Constraints; 3.5. Numerical and symbolic aspects in image fusion; 3.6. Bibliography Chapter 4. Fusion in Robotics4.1. The necessity for fusion in robotics; 4.2. Specific features of fusion in robotics; 4.2.1. Constraints on the perception system; 4.2.2. Proprioceptive and exteroceptive sensors; 4.2.3. Interaction with the operator and symbolic interpretation; 4.2.4. Time constraints; 4.3. Characteristics of the data in robotics; 4.3.1. Calibrating and changing the frame of reference; 4.3.2. Types and levels of representation of the environment; 4.4. Data fusion mechanisms; 4.5. Bibliography; Chapter 5. Information and Knowledge Representation in Fusion Problems 5.1. Introduction5.2. Processing information in fusion; 5.3. Numerical representations of imperfect knowledge; 5.4. Symbolic representation of imperfect knowledge; 5.5. Knowledge-based systems; 5.6. Reasoning modes and inference; 5.7. Bibliography; Chapter 6. Probabilistic and Statistical Methods; 6.1. Introduction and general concepts; 6.2. Information measurements; 6.3. Modeling and estimation; 6.4. Combination in a Bayesian framework; 6.5. Combination as an estimation problem; 6.6. Decision; 6.7. Other methods in detection; 6.8. An example of Bayesian fusion in satellite imagery 6.9. Probabilistic fusion methods applied to target motion analysis6.9.1. General presentation; 6.9.2. Multi-platform target motion analysis; 6.9.3. Target motion analysis by fusion of active and passive measurements; 6.9.4. Detection of a moving target in a network of sensors; 6.10. Discussion; 6.11. Bibliography; Chapter 7. Belief Function Theory; 7.1. General concept and philosophy of the theory; 7.2. Modeling; 7.3. Estimation of mass functions; 7.3.1. Modification of probabilistic models; 7.3.2. Modification of distance models 7.3.3. A priori information on composite focal elements (disjunctions) |
| Altri titoli varianti | Major probabilistic and non-probabilistic numerical approaches |
| Record Nr. | UNINA-9910877106103321 |
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications [[electronic resource] ] : 15th Iberoamerican Congress on Pattern Recognition, CIARP 2010, Sao Paulo, Brazil, November 8-11, 2010, Proceedings / / edited by Isabelle Bloch, Roberto M. Cesar, Jr
| Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications [[electronic resource] ] : 15th Iberoamerican Congress on Pattern Recognition, CIARP 2010, Sao Paulo, Brazil, November 8-11, 2010, Proceedings / / edited by Isabelle Bloch, Roberto M. Cesar, Jr |
| Edizione | [1st ed. 2010.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2010 |
| Descrizione fisica | 1 online resource (XVII, 571 p. 210 illus.) |
| Disciplina | 006.4 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Pattern recognition
Optical data processing Artificial intelligence Biometrics (Biology) Algorithms Pattern Recognition Image Processing and Computer Vision Artificial Intelligence Computer Imaging, Vision, Pattern Recognition and Graphics Biometrics Algorithm Analysis and Problem Complexity |
| Soggetto genere / forma | Kongress. |
| ISBN |
1-280-39017-4
9786613568090 3-642-16687-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- Color, Shape and Texture -- Graphs and Hypergraphs -- Biomedical Imaging -- Retrieval, Mining and Learning -- Learning, Recognition and Clustering -- Bayesian and Statistical Methods -- Coding and Compression, Video, Tracking -- Speech, Natural Language, Document -- Image Filtering and Segmentation -- Feature Extraction, Shape, Texture, Geometry and Morphology -- Face Segmentation and Recognition, Biometry -- Statistical Approaches, Learning, Classification, Mining. |
| Record Nr. | UNISA-996465974003316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||