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Machine Learning and Knowledge Discovery in Databases / European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part II / Michele Berlingerio (u. a.) / Taschenbuch - Berlingerio, Michele
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Machine Learning and Knowledge Discovery in Databases / European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part II / Michele Berlingerio (u. a.) / Taschenbuch - Taschenbuch

2019, ISBN: 9783030109271

[ED: Taschenbuch], [PU: Springer-Verlag GmbH], The three volume proceedings LNAI 11051 ¿ 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Know… Mehr…

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Machine Learning and Knowledge Discovery in Databases | European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part II | Michele Berlingerio (u. a.) | Taschenbuch - Berlingerio, Michele
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Berlingerio, Michele:

Machine Learning and Knowledge Discovery in Databases | European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part II | Michele Berlingerio (u. a.) | Taschenbuch - Taschenbuch

2019, ISBN: 9783030109271

[ED: Taschenbuch], [PU: Springer-Verlag GmbH], The three volume proceedings LNAI 11051 ¿ 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Know… Mehr…

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Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2018, Dublin, Ireland, September 10¿14, 2018, Proceedings, Part II - Berlingerio, Michele (Herausgeber); Bonchi, Francesco (Herausgeber); Ifrim, Georgiana (Herausgeber); Hurley, Neil (Herausgeber); Gärtner, Thomas (Herausgeber)
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Berlingerio, Michele (Herausgeber); Bonchi, Francesco (Herausgeber); Ifrim, Georgiana (Herausgeber); Hurley, Neil (Herausgeber); Gärtner, Thomas (Herausgeber):
Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2018, Dublin, Ireland, September 10¿14, 2018, Proceedings, Part II - neues Buch

2019

ISBN: 3030109275

1st ed. 2019 Kartoniert / Broschiert Bildbearbeitung, Bildverarbeitung, Grafik (EDV) / Bildverarbeitung, Data Mining (EDV), Datenverarbeitung / Anwendungen / Wissenschaften, EDV / Theor… Mehr…

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Machine Learning and Knowledge Discovery in Databases, Kartoniert (TB) - neues Buch

2019, ISBN: 3030109275

The three volume proceedings LNAI 11051 ¿ 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, h… Mehr…

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Machine Learning and Knowledge Discovery in Databases - neues Buch

2018, ISBN: 9783030109271

The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, h… Mehr…

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Details zum Buch
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part
Autor:
Titel:
ISBN-Nummer:

9783030109271

Detailangaben zum Buch - Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part


EAN (ISBN-13): 9783030109271
ISBN (ISBN-10): 3030109275
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2019
Herausgeber: Springer International Publishing Core >1 >T

Buch in der Datenbank seit 2019-03-16T18:57:14+01:00 (Zurich)
Detailseite zuletzt geändert am 2024-01-20T14:21:30+01:00 (Zurich)
ISBN/EAN: 9783030109271

ISBN - alternative Schreibweisen:
3-030-10927-5, 978-3-030-10927-1
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: georg thoma, gärtner, berling, berlinger, frances thomas, georgia, thomas michel, geo berlin, michele
Titel des Buches: book discovery, knowledge discovery databases, discovery dublin, machine learning

Daten vom Verlag:

Autor/in: Michele Berlingerio; Francesco Bonchi; Thomas Gärtner; Neil Hurley; Georgiana Ifrim
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part II
Verlag: Springer; Springer International Publishing
866 Seiten
Erscheinungsjahr: 2019-01-23
Cham; CH
Gedruckt / Hergestellt in Niederlande.
Sprache: Englisch
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
XXX, 866 p. 463 illus., 192 illus. in color.

BC; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; artificial intelligence; bayesian networks; big data; classification; clustering; data mining; data security; image processing; learning algorithms; machine learning; neural networks; recommender systems; semantics; signal filtering and prediction; signal processing; social networking; social networks; supervised learning; Support Vector Machines (SVM); Artificial Intelligence; Data Mining and Knowledge Discovery; Computer Vision; Computer Application in Social and Behavioral Sciences; Computing Milieux; Data and Information Security; Data Mining; Wissensbasierte Systeme, Expertensysteme; Maschinelles Sehen, Bildverstehen; Computer-Anwendungen in den Sozial- und Verhaltenswissenschaften; Informationstechnik (IT), allgemeine Themen; Computersicherheit; Netzwerksicherheit; EA

The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. 

Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.

Graphs.- Temporally Evolving Community Detection and Prediction in Content-Centric Networks.- Local Topological Data Analysis to Uncover the Global Structure of Data Approaching Graph-Structured Topologies.- Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery.- Dynamic hierarchies in temporal directed networks.- Risk-Averse Matchings over Uncertain Graph Databases.- Discovering Urban Travel Demands through Dynamic Zone Correlation in Location-Based Social Networks.- Social-Affiliation Networks: Patterns and the SOAR Model.- ONE-M: Modeling the Co-evolution of Opinions and Network Connections.- Think before You Discard: Accurate Triangle Counting in Graph Streams with Deletions.- Semi-Supervised Blockmodelling with Pairwise Guidance.- Kernel Methods.- Large-scale Nonlinear Variable Selection via Kernel Random Features.- Fast and Provably Effective Multi-view Classification with Landmark-based SVM.- Nyström-SGD: Fast Learning of Kernel-Classifiers with Conditioned Stochastic Gradient Descent.- Learning Paradigms.- Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds.- Deep Learning Architecture Search by Neuro-Cell-based Evolution with Function-Preserving Mutations.- VC-Dimension Based Generalization Bounds for Relational Learning.- Robust Super-Level Set Estimation using Gaussian Processes.- Robust Super-Level Set Estimation using Gaussian Processes.- Scalable Nonlinear AUC Maximization Methods.- Matrix and Tensor Analysis.- Lambert Matrix Factorization.- Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition.- MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds.- Block CUR: Decomposing Matrices using Groups of Columns.- Online and Active Learning.- SpectralLeader: Online Spectral Learning for Single Topic Models.- Online Learning of Weighted Relational Rules for Complex Event Recognition.- Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees.- Online Feature Selection by Adaptive Sub-gradient Methods.- Frame-based Optimal Design.- Hierarchical Active Learning with Proportion Feedback on Regions.- Pattern and Sequence Mining.- An Efficient Algorithm for Computing Entropic Measures of Feature Subsets.- Anytime Subgroup Discovery in Numerical Domains with Guarantees.- Discovering Spatio-Temporal Latent Influence in Geographical Attention Dynamics.- Mining Periodic Patterns with a MDL Criterion.- Revisiting Conditional Functional Dependency Discovery: Splitting the “C" from the “FD".- Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint.- Mining Tree Patterns with Partially Injective Homomorphisms.- Probabilistic Models and Statistical Methods.- Variational Bayes for Mixture Models with Censored Data.- Exploration Enhanced Expected Improvement for Bayesian Optimization.- A Left-to-right Algorithm for Likelihood Estimation in Gamma-Poisson Factor Analysis.- Causal Inference on Multivariate and Mixed-Type Data.- Recommender Systems.- POLAR: Attention-based CNN for One-shot Personalized Article Recommendation.- Learning Multi-granularity Dynamic Network Representations for Social Recommendation.- GeoDCF: Deep Collaborative Filtering with Multifaceted Contextual Information in Location-based Social Networks.- Personalized Thread Recommendation for MOOC Discussion Forums.- Inferring Continuous Latent Preference on Transition Intervals for Next Point-of-Interest Recommendation.- Transfer Learning.- Feature Selection for Unsupervised Domain Adaptation using Optimal Transport.- Towards more Reliable Transfer Learning.- Differentially Private Hypothesis Transfer Learning.- Information-theoretic Transfer Learning framework for Bayesian Optimisation.- A Unified Framework for Domain Adaptation using Metric Learning on Manifolds.


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