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Advances in Intelligent Data Analysis XIX: 19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26–28, 2021, Proceedings de Pedro Henriques Abreu

Advances in Intelligent Data Analysis XIX: 19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26–28, 2021, Proceedings: Lecture Notes in Computer Science, cartea 12695

Editat de Pedro Henriques Abreu, Pedro Pereira Rodrigues, Alberto Fernández, João Gama
en Limba Engleză Paperback – 13 apr 2021
This book constitutes the proceedings of the 19th International Symposium on Intelligent Data Analysis, IDA 2021, which was planned to take place in Porto, Portugal. Due to the COVID-19 pandemic the conference was held online during April 26-28, 2021. The 35 papers included in this book were carefully reviewed and selected from 113 submissions. The papers were organized in topical sections named: modeling with neural networks; modeling with statistical learning; modeling language and graphs; and modeling special data formats.
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Specificații

ISBN-13: 9783030742508
ISBN-10: 3030742504
Pagini: 600
Ilustrații: XVI, 454 p. 138 illus., 107 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.65 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Information Systems and Applications, incl. Internet/Web, and HCI

Locul publicării:Cham, Switzerland

Cuprins

Modeling with Neural Networks.- Hyperspherical Weight Uncertainty in Neural Networks.- Partially Monotonic Learning for Neural Networks.- Multiple-Manifold Generation with an Ensemble GAN and Learned Noise Prior.- Simple, Efficient and Convenient Decentralized Multi-Task Learning for Neural Networks.- Deep Hybrid Neural Networks with Improved Weighted Word Embeddings for Sentiment Analysis.- Explaining Neural Networks by Decoding Layer Activations.- Analogical Embedding for Analogy-based Learning to Rank.- HORUS-NER: A Multimodal Named Entity Recognition Framework for Noisy Data.- Modeling with Statistical Learning.- Incremental Search Space Construction for Machine Learning Pipeline Synthesis.- Adversarial Vulnerability of Active Transfer Learning.- Revisiting Non-Specific Syndromic Surveillance.- Gradient Ascent for Best Response Regression.- Intelligent Structural Damage Detection: a Federated Learning Approach.- Composite surrogate for likelihood-freeBayesian optimisation in high-dimensional settings of activity-based transportation models.- Active Selection of Classification Features.- Feature Selection for Hierarchical Multi-Label Classification.- Bandit Algorithm for Both Unknown Best Position and Best Item Display on Web Pages.- Performance prediction for hardware-software configurations: A case study for video games.- avatar | Automated Feature Wrangling for Machine Learning.- Modeling Language and Graphs.- Semantically Enriching Embeddings of Highly In ectable Verbs for Improving Intent Detection in a Romanian Home Assistant Scenario.- BoneBert: A BERT-based Automated Information Extraction System of Radiology Reports for Bone Fracture Detection and Diagnosis.- Linking the Dynamics of User Stance to the Structure of Online Discussions.- Unsupervised Methods for the Study of Transformer Embeddings.- A Framework for Authorial Clustering of Shorter Texts in Latent Semantic Spaces.- DeepGG: a Deep Graph Generator.- SINr: fast computing of Sparse Interpretable Node Representations is not a sin.- Detection of contextual anomalies in attributed graphs.- Ising-Based Louvain Method: Clustering Large Graphs with Specialized Hardware.- Modeling Special Data Formats.- Reducing Negative Impact of Noise in Boolean Matrix Factorization with Association Rules.- Z-Hist: A Temporal Abstraction of Multivariate Histogram Snapshots.- muppets: Multipurpose Table Segmentation.- SpLyCI: Integrating Spreadsheets by Recognising and Solving Layout Constraints.- RTL: A Robust Time Series Labeling Algorithm.- The Compromise of Data Privacy in Predictive Performance.- Efficient Privacy Preserving Distributed K-Means for Non-IID Data.