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Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I - Michele Berlingerio
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Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I - Livro de bolso

2019, ISBN: 3030109240

[EAN: 9783030109240], Neubuch, [SC: 0.0], [PU: Springer International Publishing], BILDBEARBEITUNG; BILDVERARBEITUNG; GRAFIK (EDV) / COMPUTER PC-HARDWARE; DATA MINING (EDV); DATENVERARBEI… mais…

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Machine Learning And Knowledge Discovery In Databases by Michele Berlingerio Paperback | Indigo Chapters
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Machine Learning And Knowledge Discovery In Databases by Michele Berlingerio Paperback | Indigo Chapters - novo libro

2018, ISBN: 9783030109240

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… mais…

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Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2018, Dublin, Ireland, September 10¿14, 2018, Proceedings, Part I - 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 I - novo libro

2019

ISBN: 3030109240

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Machine Learning and Knowledge Discovery in Databases
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Machine Learning and Knowledge Discovery in Databases - Livro de bolso

2019, ISBN: 9783030109240

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Machine Learning and Knowledge Discovery in Databases - Michele Berlingerio; Francesco Bonchi; Thomas Gärtner; Neil Hurley; Georgiana Ifrim
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Michele Berlingerio; Francesco Bonchi; Thomas Gärtner; Neil Hurley; Georgiana Ifrim:
Machine Learning and Knowledge Discovery in Databases - Livro de bolso

2019, ISBN: 9783030109240

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Machine Learning and Knowledge Discovery in Databases
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9783030109240

Dados detalhados do livro - Machine Learning and Knowledge Discovery in Databases


EAN (ISBN-13): 9783030109240
ISBN (ISBN-10): 3030109240
Livro de bolso
Ano de publicação: 2019
Editor/Editora: Springer International Publishing

Livro na base de dados desde 2019-02-09T15:57:32+00:00 (Lisbon)
Página de detalhes modificada pela última vez em 2023-03-30T12:52:22+01:00 (Lisbon)
Número ISBN/EAN: 9783030109240

Número ISBN - Ortografia alternativa:
3-030-10924-0, 978-3-030-10924-0
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: georg thoma, gärtner, berling, berlinger, frances thomas, georgia, thomas michel, geo berlin
Título do livro: book discovery, knowledge discovery databases, discovery dublin, knowledge learning

Dados da editora

Autor: Michele Berlingerio; Francesco Bonchi; Thomas Gärtner; Neil Hurley; Georgiana Ifrim
Título: 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 I
Editora: Springer; Springer International Publishing
740 Páginas
Ano de publicação: 2019-01-18
Cham; CH
Impresso / Feito em
Língua: Inglês
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
POD
XXXVIII, 740 p. 451 illus., 159 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.

Adversarial Learning.- Image Anomaly Detection with Generative Adversarial Networks.- Image-to-Markup Generation via Paired Adversarial Learning.- Toward an Understanding of Adversarial Examples in Clinical Trials.- ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector.- Anomaly and Outlier Detection.- GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid.- Incorporating Privileged Information to Unsupervised Anomaly Detection.- L1-Depth Revisited: A Robust Angle-based Outlier Factor in High-dimensional Space.- Beyond Outlier Detection: LookOut for Pictorial Explanation.- Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier Features.- Group Anomaly Detection using Deep Generative Models.- Applications.- A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements.- Face-Cap: Image Captioning using Facial Expression Analysis.- Pedestrian Trajectory Prediction with Structured Memory Hierarchies.- Classification.- Multiple Instance Learning with Bag-level Randomized Trees.- One-class Quantification.- Deep F-Measure Maximization in Multi-Label Classification: A Comparative Study.- Ordinal Label Proportions.- AWX: An Integrated Approach to Hierarchical-Multilabel Classification.- Clustering and Unsupervised Learning.- Clustering in the Presence of Concept Drift.- Time Warp Invariant Dictionary Learning for Time Series Clustering.- How Your Supporters and Opponents Define Your Interestingness.- Deep Learning.- Efficient Decentralized Deep Learning by Dynamic Model Averaging.- Using Supervised Pretraining to Improve Generalization of Neural Networks on Binary Classification Problems.- Towards Efficient Forward Propagation on Resource-Constrained Systems.- Auxiliary Guided Autoregressive Variational Autoencoders.- Cooperative Multi-Agent Policy Gradient.- Parametric t-Distributed Stochastic Exemplar-centered Embedding.- Joint autoencoders: a flexible meta-learning framework.- Privacy Preserving Synthetic Data Release Using Deep Learning.- On Finer Control of Information Flow in LSTMs.- MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes.- Ontology alignment based on word embedding and random forest classification.- Domain Adaption in One-Shot Learning.- Ensemble Methods.- Axiomatic Characterization of AdaBoost and the Multiplicative Weight Update Procedure.- Modular Dimensionality Reduction.- Constructive Aggregation and its Application to Forecasting with Dynamic Ensembles.- MetaBags: Bagged Meta-Decision Trees for Regression.- Evaluation.- Visualizing the Feature Importance for Black Box Models.- Efficient estimation of AUC in a sliding window.- Controlling and visualizing the precision-recall tradeoff for external performance indices.- Evaluation Procedures for Forecasting with Spatio-Temporal Data.- A Blended Metric for Multi-label Optimisation and Evaluation.


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