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[null,"1.","Record Nr.",null,"UNINA9910508099303321"]
[null,null,"Autore",null,"Lagomarsini, Claudio"]
[null,null,"Titolo",null,"Il Graal e i cavalieri della Tavola Rotonda : guida ai romanzi francesi in prosa del Duecento / Claudio Lagomarsini"]
[null,null,"Pubbl/distr/stampa",null,"Bologna, : Il mulino, 2020"]
[null,null,"ISBN",null,"978-88-15-28608-6"]
[null,null,"Descrizione fisica",null,"136 p. ; 20 cm"]
[null,null,"Collana",null,"Introduzioni. Filologia"]
[null,null,"Disciplina",null,"841.03309"]
[null,null,"Collocazione",null,"841.1 LAG 1"]
[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,"UNISA996464435203316"]
[null,null,"Titolo",null,"Intelligent information and database systems : 13th Asian Conference, ACIIDS 2021, Phuket, Thailand, April 7-10, 2021, proceedings / / Ngoc Thanh Nguyen [and three others] editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-73280-0"]
[null,null,"Descrizione fisica",null,"1 online resource (xxxi, 867 pages) : illustrations"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.12672"]
[null,null,"Disciplina",null,"006.33"]
[null,null,"Soggetti",null,"Expert systems (Computer science)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Conference Organization -- Contents -- Data Mining Methods and Applications -- Mining Partially-Ordered Episode Rules in an Event Sequence -- 1 Introduction -- 2 Problem Definition -- 3 The POERM Algorithm -- 4 Experimental Evaluation -- 5 Conclusion -- References -- Investigating Crossover Operators in Genetic Algorithms for High-Utility Itemset Mining -- 1 Introduction -- 2 Preliminaries -- 3 The Headless Chicken Test for HUIM Using GAs -- 4 Experiments and Results -- 4.1 Runtime -- 4.2 Discovered HUIs -- 4.3 Convergence -- 5 Conclusion -- References -- Complexes of Low Dimensional Linear Classifiers with L1 Margins -- 1 Introduction -- 2 Linear Separability of Feature Vectors -- 3 Dual Hyperplanes and Vertices in the Parameter Space -- 4 Perceptron Criterion Function -- 5 Basis Exchange Algorithms -- 6 Reduced Criterion Functions -- 7 Complexes of Linear Classifiers -- 8 Concluding Remarks -- References -- Automatic Identification of Bird Species from Audio -- 1 Introduction -- 2 State of Art -- 2.1 Preprocessing and Feature Extraction -- 2.2 Deep Learning for Sound Classification -- 2.3 Other Approaches -- 3 Our Approach -- 3.1 Pre-processing -- 3.2 Feature Extraction -- 3.3 Deep Learning Model Architectures -- 4 Experiments and Evaluation -- 4.1 Evaluation Metrics -- 4.2 Experiments -- 4.3 Best Results -- 5 Conclusions and Future Work -- References -- Session Based Recommendations Using Recurrent Neural Networks - Long Short-Term Memory -- 1 Introduction -- 2 Methods of Content Recommendation -- 2.1 Multi-Layer Perceptron -- 2.2 Autoencoders -- 2.3 Boltzmann Machines -- 3 Evaluation of Recommendation "]
[{"$nulls":4},"Systems -- 3.1 MAE -- 3.2 MSE -- 3.3 Precision -- 3.4 Recall -- 4 Using the LSTM as a Recommender System -- 4.1 Recurrent Neural Networks -- 4.2 Long Short-Term Memory -- 4.3 Data Preparation -- 4.4 Model Definition."]
[{"$nulls":4},"5 Results and Discussions -- 5.1 Experiment -- 5.2 Recall and Precision Results -- 6 Conclusions and Future Work -- References -- UVDS: A New Dataset for Traffic Forecasting with Spatial-Temporal Correlation -- 1 Introduction -- 2 Literature Review -- 2.1 Traffic Forecasting Using Graph Neural Network -- 2.2 Public Traffic Flow Datasets -- 3 UVDS: Urban Vehicle Detection System Dataset -- 3.1 Data Description and Research Challenges -- 3.2 Graph Construction -- 4 Baseline Results and Discussion -- 4.1 Baseline Models -- 4.2 Experimental Results -- 4.3 Open Research Issues Using UVDS Dataset -- 5 Conclusion -- References -- A Parallelized Frequent Temporal Pattern Mining Algorithm on a Time Series Database -- 1 Introduction -- 2 A Frequent Temporal Inter-object Pattern Mining Task -- 3 The Parallelized Frequent Temporal Inter-object Pattern Mining Algorithm on a Time Series Database -- 3.1 BranchTree -- 3.2 From BranchTree to PTP -- 4 Empirical Evaluation -- 5 Conclusion -- References -- An Efficient Approach for Mining High-Utility Itemsets from Multiple Abstraction Levels -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Proposed Algorithm -- 5 Evaluation Studies -- 6 Conclusion and Future Work -- References -- Mining Class Association Rules on Dataset with Missing Data -- 1 Introduction -- 2 Related Work -- 2.1 Type of Missing Data -- 2.2 Imputation Method -- 2.3 Mining Class Association Rules -- 3 Model for Mining Class Association Rules with Incomplete Datasets -- 3.1 Definition -- 3.2 Method -- 3.3 Training Process -- 3.4 Application Process -- 4 Experimental Studies -- 5 Conclusion and Future Work -- References -- Machine Learning Methods -- Clustering Count Data with Stochastic Expectation Propagation -- 1 Introduction -- 2 The Exponential-Family Approximation to MSD Distribution -- 3 Stochastic Expectation Propagation."]
[{"$nulls":4},"4 EMSD Mixture Model -- 4.1 Clustering Model -- 4.2 Parameter Learning -- 5 Experimental Results -- 5.1 Synthetic Dataset -- 5.2 Sentiment Analysis -- 6 Conclusions -- References -- Entropy-Based Variational Learning of Finite Generalized Inverted Dirichlet Mixture Model -- 1 Introduction -- 2 Model Specification -- 2.1 Finite Generalized Inverted Dirichlet Mixture -- 3 Model Learning with Variational Inference -- 4 Entropy-Based Variational Model Learning -- 4.1 Differential Entropy Estimation -- 4.2 MeanNN Entropy Estimator -- 5 Experimental Results -- 5.1 Breast Cancer -- 5.2 Image Analysis -- 6 Conclusion -- References -- Mixture-Based Unsupervised Learning for Positively Correlated Count Data -- 1 Introduction -- 2 Negative Multinomial Mixture Model -- 2.1 Maximum Likelihood Parameter Estimation -- 3 Model Selection -- 4 Experimental Results -- 5 Conclusion -- References -- Phase Prediction of Multi-principal Element Alloys Using Support Vector Machine and Bayesian Optimization -- 1 Introduction -- 2 Description of Datasets -- 3 Building Prediction Models with SVM -- 3.1 Defining Predictors and an Outcome -- 3.2 Tuning Hyperparameters Using Bayesian Optimization -- 3.3 Cross-Validation of Prediction Models Employing Tuned Hyperparameters -- 4 Statistical Analysis of Experimental Results -- 5 Conclusions -- References -- VEGAS: A Variable Length-Based Genetic Algorithm for Ensemble Selection in Deep Ensemble Learning -- 1 Introduction -- 2 Background and Related Work -- 2.1 Ensemble Learning and Ensemble Selection -- 2.2 Variable Learning Encoding in Evolutionary Computation -- 3 Proposed Method -- 3.1 Ensemble "]
[{"$nulls":4},"Selection for Deep Ensemble Systems -- 3.2 Optimization Problems and Algorithm -- 4 Experimental Studies -- 4.1 Experimental Settings -- 4.2 Comparison to the Benchmark Algorithms -- 4.3 Discussions -- 5 Conclusions -- References."]
[{"$nulls":4},"Demand Forecasting for Textile Products Using Statistical Analysis and Machine Learning Algorithms -- 1 Introducción -- 2 Related Works -- 3 Materials and Methods -- 3.1 Case Study Description -- 3.2 Statistical Analysis -- 3.3 Demand Forecasting -- 4 Results and Discussion -- 5 Conclusions and Future Work -- References -- Parallelization of Reinforcement Learning Algorithms for Video Games -- 1 Introduction -- 2 Background and Related Work -- 2.1 Reinforcement Learning -- 2.2 Methods for Solving RL Problems -- 2.3 Parallelization of RL Methods -- 3 Experiment -- 3.1 Environment and Algorithms Parameters -- 3.2 Measures -- 4 Results -- 4.1 Influence of Parallelization on Training Times -- 4.2 Throughput Measurements -- 4.3 System Performance Measurements -- 5 Conclusions -- References -- Decision Support and Control Systems -- A Gap-Based Memetic Differential Evolution (GaMeDE) Applied to Multi-modal Optimisation - Using Multi-objective Optimization Concepts -- 1 Introduction -- 2 Contribution and Motivation -- 3 Approach -- 3.1 Archive Management -- 3.2 Gap Selection Operator -- 3.3 Two-Phased GaMeDE -- 3.4 Clustering -- 3.5 Local Optimization -- 4 Experiments -- 4.1 Setup -- 4.2 Metrics -- 4.3 Results -- 4.4 Discussion -- 5 Conclusions -- References -- Simulating Emergency Departments Using Generalized Petri Nets -- 1 Introduction -- 2 Modelling Approach -- 2.1 Data Collection and Processing -- 2.2 Simulation Model -- 3 Simulation Modeling -- 3.1 The Proposed GSPN-Based Model -- 3.2 The Hierarchical GSPN Model of the ED -- 4 Simulation Results and Discussion -- 4.1 Model Validation -- 4.2 Forecasting Patients' Waiting Times -- 5 Conclusion -- References -- What Do You Know About Your Network: An Empirical Study of Value Network Awareness in E-commerce -- 1 Introduction -- 2 Theoretical Background -- 2.1 Value Network -- 2.2 Network Awareness."]
[{"$nulls":4},"3 Overview of the Studies -- 3.1 Sampling of Study 1 - Suppliers and Complementors -- 3.2 Sampling of Study 2 - Online Sellers -- 3.3 Sampling of Study 3 - End Customers -- 4 Results -- 5 Discussion -- 6 Limitations and Future Research -- References -- A Robust Approach to Employee Competences in Project Management -- 1 Introduction -- 2 Illustrative Example -- 3 Model of Employee Competence Configuration Problem -- 4 Method for Verifying the Set of Employee Competences -- 5 Computational Examples -- 6 Conclusions -- References -- Key Aspects of Customer Intelligence in the Era of Massive Data -- 1 Introduction -- 2 Literature Review -- 2.1 Research Process -- 2.2 The Revolution of Customer Intelligence -- 3 Theoretical Framework -- 3.1 Resource-Based Theory -- 3.2 A Resource-Based Framework of Customer Intelligence Management -- 4 Key Aspects of Customer Intelligence in the Era of Massive Data -- 4.1 Product/Service Aspect -- 4.2 Price Aspect -- 4.3 Promotion Aspect -- 4.4 Place Aspect -- 4.5 People Aspect -- 4.6 Process Aspect -- 4.7 Physical Evidence Aspect -- 5 Conclusion -- References -- How Spatial Data Analysis Can Make Smart Lighting Smarter -- 1 Introduction -- 2 Problem Statement -- 3 Methodology -- 4 Implementation -- 4.1 Mapping of Linestrings to Road Shapes -- 4.2 Assignment of Lights to Lit Areas -- 5 Results -- 5.1 Quality of Modelled Situations -- 5.2 Estimation Efficiency -- 6 Conclusions -- References -- Natural Language Processing -- Convolutional Neural Networks for Web Documents Classification -- 1 Introduction -- 2 Related Work -- 3 Text Classification -- 4 Methodology -- 4.1 Dataset -- 4.2 Model -- 4.3 "]
[{"$nulls":4},"Evaluation -- 5 Experimental Results -- 5.1 Baseline Setup -- 5.2 Multi-class Problem -- 5.3 Input Variation -- 5.4 Embedding Vectors Variation -- 5.5 Language Transfer Learning -- 6 Conclusions -- References."]
[{"$nulls":4},"Sequential Model-Based Optimization for Natural Language Processing Data Pipeline Selection and Optimization."]