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2: Tree-Based Methods and Extensions / Michel Denuit, Donatien Hainaut, Julien Trufin
| 2: Tree-Based Methods and Extensions / Michel Denuit, Donatien Hainaut, Julien Trufin |
| Autore | Denuit, Michel |
| Pubbl/distr/stampa | Cham, : Springer, 2020 |
| Descrizione fisica | x, 228 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Hainaut, Donatien
Trufin, Julien |
| Soggetto topico |
62-XX - Statistics [MSC 2020]
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 62J12 - Generalized linear models (logistic models) [MSC 2020] 62M10 - Time series, auto-correlation, regression, etc. in statistics (GARCH) [MSC 2020] 91-XX - Game theory, economics, finance, and other social and behavioral sciences [MSC 2020] 62H30 - Classification and discrimination; cluster analysis (statistical aspects) [MSC 2020] 62P05 - Applications of statistics to actuarial sciences and financial mathematics [MSC 2020] 91G05 - Actuarial mathematics [MSC 2020] |
| Soggetto non controllato |
Actuarial modeling
Insurance risk classification Machine learning Supervised learning Tree-based methods for insurance |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0249059 |
Denuit, Michel
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| Cham, : Springer, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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3: Neural Networks and Extensions / Michel Denuit, Donatien Hainaut, Julien Trufin
| 3: Neural Networks and Extensions / Michel Denuit, Donatien Hainaut, Julien Trufin |
| Autore | Denuit, Michel |
| Pubbl/distr/stampa | Cham, : Springer, 2019 |
| Descrizione fisica | xiii, 250 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Hainaut, Donatien
Trufin, Julien |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
62-XX - Statistics [MSC 2020] 62M45 - Neural nets and related approaches to inference from stochastic processes [MSC 2020] 62P05 - Applications of statistics to actuarial sciences and financial mathematics [MSC 2020] |
| Soggetto non controllato |
Actuarial modeling
Deep learing for insurance Insurance risk classification Machine learning Neural networks |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0126843 |
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Denuit, Michel
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| Cham, : Springer, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods / Shan Liu, ... [et al.]
| 3D Point Cloud Analysis : Traditional, Deep Learning, and Explainable Machine Learning Methods / Shan Liu, ... [et al.] |
| Pubbl/distr/stampa | Cham, : Springer, 2021 |
| Descrizione fisica | xiv, 146 p. : ill. ; 24 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 68T45 - Machine vision and scene understanding [MSC 2020] 68T07 - Artificial neural networks and deep learning [MSC 2020] |
| Soggetto non controllato |
3D computer vision
3D object detection 3D object recognition Deep Learning Explainable machine learning Machine learning ModelNet40 Point cloud analysis Point cloud classification Point cloud part segmentation Point cloud registration PointHop R-PointHop Saab transform ShapeNet Successive subspace learning Unsupervised learning |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0274505 |
| Cham, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Advanced Data Analysis in Neuroscience : Integrating Statistical and Computational Models / Daniel Durstewitz
| Advanced Data Analysis in Neuroscience : Integrating Statistical and Computational Models / Daniel Durstewitz |
| Autore | Durstewitz, Daniel |
| Pubbl/distr/stampa | Cham, : Springer, 2017 |
| Descrizione fisica | xxv, 292 p. : ill. ; 24 cm |
| Soggetto topico |
92C20 - Neural biology [MSC 2020]
62R07 - Statistical aspects of big data and data science [MSC 2020] 68T09 - Computational aspects of data analysis and big data [MSC 2020] |
| Soggetto non controllato |
Bootstrap methods
Change point analysis Clustering Dimensionality reduction Machine learning Multiple testing Multivariate maps and recurrent neural networks Multivariate statistics Neural time series Nonlinear dynamical systems Nonlinear oscillations Nonparametric time series modeling Principal component analysis Reconstructing state spaces from experimental data Statistical methods in neuroscience Statistical parameter estimation Unsupervised clustering |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0123545 |
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Durstewitz, Daniel
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| Cham, : Springer, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Advanced Digital Auditing [[electronic resource] ] : Theory and Practice of Auditing Complex Information Systems and Technologies / / edited by Egon Berghout, Rob Fijneman, Lennard Hendriks, Mona de Boer, Bert-Jan Butijn
| Advanced Digital Auditing [[electronic resource] ] : Theory and Practice of Auditing Complex Information Systems and Technologies / / edited by Egon Berghout, Rob Fijneman, Lennard Hendriks, Mona de Boer, Bert-Jan Butijn |
| Autore | Berghout Egon |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2023 |
| Descrizione fisica | 1 online resource (XIV, 256 p. 63 illus., 42 illus. in color.) |
| Disciplina | 658.05 |
| Collana | Progress in IS |
| Soggetto topico |
Business information services
Software engineering—Management Risk management Data protection IT in Business Software Management IT Risk Management Data and Information Security |
| Soggetto non controllato |
Complex systems
Blockchain Machine learning Algorithm assurance Audit trails Fintech Cloud security Advanced information technology Deep learning Public cloud auditing Cloud computing Business process modelling IT assurance Auditing criteria Auditing methodologies Auditing artificial intelligence Auditing with AI Cyber security |
| ISBN | 3-031-11089-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Auditing Advanced Information Systems and Technologies in a Modern Digital World -- Chapter 2. Auditing Complexity -- Chapter 3. Introduction to Advanced Information Technology -- Chapter 4. The Intercompany Settlement Blockchain: Benefits, Risks and Internal IT Controls -- Chapter 5. Understanding Algorithms -- Chapter 6. Keeping Control on Deep Learning Image Recognition Algorithms -- Chapter 7. Algorithm Assurance: Auditing Applications of Artificial Intelligence -- Chapter 8. Demystifying Public Cloud Auditing for IT Auditors -- Chapter 9. Process Mining for Detailed Process Analysis. |
| Record Nr. | UNINA-9910623995503321 |
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Berghout Egon
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| Cham, : Springer Nature, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in Intelligent Data Analysis XVIII [[electronic resource] ] : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings / / edited by Michael R. Berthold, Ad Feelders, Georg Krempl
| Advances in Intelligent Data Analysis XVIII [[electronic resource] ] : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings / / edited by Michael R. Berthold, Ad Feelders, Georg Krempl |
| Autore | Berthold Michael |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XIV, 588 p. 210 illus., 132 illus. in color.) |
| Disciplina | 005.74 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Database management
Data mining Computers Machine learning Computer organization Database Management Data Mining and Knowledge Discovery Computing Milieux Machine Learning Computer Systems Organization and Communication Networks |
| Soggetto non controllato |
Database Management
Data Mining and Knowledge Discovery Computing Milieux Machine Learning Computer Systems Organization and Communication Networks open access data mining learning systems classification clustering semantics learning algorithms supervised learning association rules social networks graphic methods neural networks artificial intelligence computer vision correlation analysis databases education engineering graph theory image analysis Databases Database programming Data mining Expert systems / knowledge-based systems Information technology: general issues Machine learning Computer networking & communications |
| ISBN | 3-030-44584-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder -- Dual Sequential Variational Autoencoders for Fraud Detection -- A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks -- Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams -- GraphMDL: Graph Pattern Selection Based on Minimum Description Length -- Towards Content Sensitivity Analysis -- Gibbs Sampling Subjectively Interesting Tiles -- Even Faster Exact k-Means Clustering -- Ising-Based Consensus Clustering on Special Purpose Hardware -- Transfer Learning by Learning Projections from Target to Source -- Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs -- Towards Evaluation of CNN Performance in Semantically Meaningful Latent Spaces -- Vouw: Geometric Pattern Mining Using the MDL Principle -- A Consensus Approach to Improve NMF Document Clustering -- Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams -- Widening for MDL-Based Retail Signature Discovery -- Addressing the Resolution Limit and the Field of View Limit in Community Mining -- Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics -- Adversarial Attacks Hidden in Plain Sight -- Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code -- Overlapping Hierarchical Clustering (OHC) -- Digital Footprints of International Migration on Twitter -- Percolation-Based Detection of Anomalous Subgraphs in Complex Networks -- A Late-Fusion Approach to Community Detection in Attributed Networks -- Reconciling Predictions in the Regression Setting: an Application to Bus Travel Time Prediction -- A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization -- Actionable Subgroup Discovery and Urban Farm Optimization -- AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model -- Detection of Derivative Discontinuities in Observational Data -- Improving Prediction with Causal Probabilistic Variables -- DO-U-Net for Segmentation and Counting -- Enhanced Word Embeddings for Anorexia Nervosa Detection on Social Media -- Event Recognition Based on Classification of Generated Image Captions -- Human-to-AI Coach: Improving Human Inputs to AI Systems -- Aleatoric and Epistemic Uncertainty with Random Forests -- Master your Metrics with Calibration -- Supervised Phrase-Boundary Embeddings -- Predicting Remaining Useful Life with Similarity-Based Priors -- Orometric Methods in Bounded Metric Data -- Interpretable Neuron Structuring with Graph Spectral Regularization -- Comparing the Preservation of Network Properties by Graph Embeddings -- Making Learners (More) Monotone -- Combining Machine Learning and Simulation to a Hybrid Modelling Approach -- LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification -- Angle-Based Crowding Degree Estimation for Many-Objective Optimization. |
| Record Nr. | UNISA-996418219903316 |
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Berthold Michael
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| Cham, : Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Advances in Intelligent Data Analysis XVIII [[electronic resource] ] : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings / / edited by Michael R. Berthold, Ad Feelders, Georg Krempl
| Advances in Intelligent Data Analysis XVIII [[electronic resource] ] : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings / / edited by Michael R. Berthold, Ad Feelders, Georg Krempl |
| Autore | Berthold Michael |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XIV, 588 p. 210 illus., 132 illus. in color.) |
| Disciplina | 005.74 |
| Collana | Information Systems and Applications, incl. Internet/Web, and HCI |
| Soggetto topico |
Database management
Data mining Computers Machine learning Computer organization Database Management Data Mining and Knowledge Discovery Computing Milieux Machine Learning Computer Systems Organization and Communication Networks |
| Soggetto non controllato |
Database Management
Data Mining and Knowledge Discovery Computing Milieux Machine Learning Computer Systems Organization and Communication Networks open access data mining learning systems classification clustering semantics learning algorithms supervised learning association rules social networks graphic methods neural networks artificial intelligence computer vision correlation analysis databases education engineering graph theory image analysis Databases Database programming Data mining Expert systems / knowledge-based systems Information technology: general issues Machine learning Computer networking & communications |
| ISBN | 3-030-44584-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder -- Dual Sequential Variational Autoencoders for Fraud Detection -- A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks -- Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams -- GraphMDL: Graph Pattern Selection Based on Minimum Description Length -- Towards Content Sensitivity Analysis -- Gibbs Sampling Subjectively Interesting Tiles -- Even Faster Exact k-Means Clustering -- Ising-Based Consensus Clustering on Special Purpose Hardware -- Transfer Learning by Learning Projections from Target to Source -- Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs -- Towards Evaluation of CNN Performance in Semantically Meaningful Latent Spaces -- Vouw: Geometric Pattern Mining Using the MDL Principle -- A Consensus Approach to Improve NMF Document Clustering -- Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams -- Widening for MDL-Based Retail Signature Discovery -- Addressing the Resolution Limit and the Field of View Limit in Community Mining -- Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics -- Adversarial Attacks Hidden in Plain Sight -- Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code -- Overlapping Hierarchical Clustering (OHC) -- Digital Footprints of International Migration on Twitter -- Percolation-Based Detection of Anomalous Subgraphs in Complex Networks -- A Late-Fusion Approach to Community Detection in Attributed Networks -- Reconciling Predictions in the Regression Setting: an Application to Bus Travel Time Prediction -- A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization -- Actionable Subgroup Discovery and Urban Farm Optimization -- AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model -- Detection of Derivative Discontinuities in Observational Data -- Improving Prediction with Causal Probabilistic Variables -- DO-U-Net for Segmentation and Counting -- Enhanced Word Embeddings for Anorexia Nervosa Detection on Social Media -- Event Recognition Based on Classification of Generated Image Captions -- Human-to-AI Coach: Improving Human Inputs to AI Systems -- Aleatoric and Epistemic Uncertainty with Random Forests -- Master your Metrics with Calibration -- Supervised Phrase-Boundary Embeddings -- Predicting Remaining Useful Life with Similarity-Based Priors -- Orometric Methods in Bounded Metric Data -- Interpretable Neuron Structuring with Graph Spectral Regularization -- Comparing the Preservation of Network Properties by Graph Embeddings -- Making Learners (More) Monotone -- Combining Machine Learning and Simulation to a Hybrid Modelling Approach -- LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification -- Angle-Based Crowding Degree Estimation for Many-Objective Optimization. |
| Record Nr. | UNINA-9910404119303321 |
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Berthold Michael
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| Cham, : Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in speech recognition
| Advances in speech recognition |
| Autore | Shabtai Noam |
| Pubbl/distr/stampa | IntechOpen, 2010 |
| Descrizione fisica | 1 online resource (178 pages) |
| Soggetto topico | COMPUTERS / Artificial Intelligence / General |
| Soggetto non controllato | Machine learning |
| ISBN | 953-51-5949-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910138264003321 |
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Shabtai Noam
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| IntechOpen, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithmic Governance : Politics and Law in the Post-Human Era / Ignas Kalpokas
| Algorithmic Governance : Politics and Law in the Post-Human Era / Ignas Kalpokas |
| Autore | Kalpokas, Ignas |
| Pubbl/distr/stampa | Cham, : Palgrave Macmillan, 2019 |
| Descrizione fisica | ix, 120 p. ; 24 cm |
| Soggetto topico |
91Bxx - Mathematical economics [MSC 2020]
91-XX - Game theory, economics, finance, and other social and behavioral sciences [MSC 2020] |
| Soggetto non controllato |
Algorithmic governance and consumer satisfaction
Algorithmic politics Algorithms in contemporary society Balance of embeddedness Big Data Commodification Datafication Dis-imagined communities Human agency Human-digital interrelationality International Covenant on Civil and Political Right ICCPR Internet of Things Machine learning Platform economy Political decision-making Posthuman law Posthumanism Regulatory function of algorithms Universal Declaration of Human Rights UDHR |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0126687 |
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Kalpokas, Ignas
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| Cham, : Palgrave Macmillan, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Algorithmic Learning in a Random World / Vladimir Vovk, Alexander Gammerman, Glenn Shafer
| Algorithmic Learning in a Random World / Vladimir Vovk, Alexander Gammerman, Glenn Shafer |
| Autore | Vovk, Vladimir |
| Edizione | [2. ed] |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xxvi, 476 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Gammerman, Alexander
Shafer, Glenn |
| Soggetto non controllato |
Conformal prediction
Conformal predictive distributions Conformal testing Machine learning Nonparametric Statistics Online compression modeling Venn prediction |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0276839 |
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Vovk, Vladimir
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| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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