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Advances in Knowledge Discovery and Data Mining 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part III

Aus der Reihe Lecture Notes in Computer Science

89,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

08.05.2021

Abbildungen

XXIII, 434 p. 142 illus., 117 illus. in color.

Herausgeber

Kamal Karlapalem + weitere

Verlag

Springer

Seitenzahl

434

Maße (L/B/H)

23,5/15,5/2,5 cm

Gewicht

692 g

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-3-030-75767-0

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

08.05.2021

Abbildungen

XXIII, 434 p. 142 illus., 117 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

434

Maße (L/B/H)

23,5/15,5/2,5 cm

Gewicht

692 g

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-3-030-75767-0

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Advances in Knowledge Discovery and Data Mining
  • Produktbild: Advances in Knowledge Discovery and Data Mining
  • Representation Learning and Embedding.- Episode Adaptive Embedding Networks for Few-shot Learning.- Universal Representation for Code.- Self-supervised Adaptive Aggregator Learning on Graph.- A Fast Algorithm for Simultaneous Sparse Approximation.- STEPs-RL: Speech-Text Entanglement for Phonetically Sound Representation Learning.- RW-GCN: Training Graph Convolution Networks with biased random walk for Semi-Supervised Classification.- Loss-aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models.- SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network.- VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning.- Self-supervised Graph Representation Learning with Variational Inference.- Manifold Approximation and Projection by Maximizing Graph Information.- Learning Attention-based Translational Knowledge Graph Embedding via Nonlinear Dynamic Mapping.- Multi-Grained Dependency Graph Neural Network for Chinese Open Information Extraction.- Human-Understandable Decision Making for Visual Recognition.- LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding.- Transferring Domain Knowledge with an Adviser in Continuous Tasks.- Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach.- Quality Control for Hierarchical Classification with Incomplete Annotations.- Learning from Data.- Learning Discriminative Features using Multi-label Dual Space.- AutoCluster: Meta-learning Based Ensemble Method for Automated Unsupervised Clustering.- BanditRank: Learning to Rank Using Contextual Bandits.- A compressed and accelerated SegNet for plant leaf disease segmentation: A Differential Evolution based approach.- Meta-Context Transformers for Domain-Specific Response Generation.- A Multi-task Kernel Learning Algorithm for Survival Analysis.- Meta-data Augmentation based Search Strategy through Generative Adversarial Network for AutoML Model Selection.- Tree-Capsule: Tree-Structured Capsule Network for Improving Relation Extraction.- Rule Injection-based Generative Adversarial Imitation Learning for Knowledge Graph Reasoning.- Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition.- Reinforced Natural Language Inference for Distantly Supervised Relation Classification.- SaGCN: Structure-aware Graph Convolution Network for Document-level Relation Extraction.- Addressing the class imbalance problem in medical image segmentation via accelerated Tversky loss function.- Incorporating Relational Knowledge in Explainable Fake News Detection.- Incorporating Syntactic Information into Relation Representations for Enhanced Relation Extraction.