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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 I

Aus der Reihe Lecture Notes in Computer Science

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

09.05.2021

Abbildungen

XXXV, 834 p. 30 illus.

Herausgeber

Kamal Karlapalem + weitere

Verlag

Springer

Seitenzahl

834

Maße (L/B/H)

23,5/15,5/4,7 cm

Gewicht

1293 g

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-3-030-75761-8

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

09.05.2021

Abbildungen

XXXV, 834 p. 30 illus.

Herausgeber

Verlag

Springer

Seitenzahl

834

Maße (L/B/H)

23,5/15,5/4,7 cm

Gewicht

1293 g

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-3-030-75761-8

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
  • Applications of Knowledge Discovery.- Fuzzy World:A Tool Training Agent from Concept Cognitive to Logic Inference.- Collaborative Reinforcement Learning Framework to Model Evolution of Cooperation in Sequential Social Dilemmas.- SIGTRAN: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks.- VOA*: Fast Angle-Based Outlier Detection Over High-Dimensional Data Streams.- Learning Probabilistic Latent Structure for Outlier Detection from Multi-View Data.- GLAD-PAW: Graph-based Log Anomaly Detection by Position Aware Weighted Graph Attention Network.- CubeFlow: Money Laundering Detection with Coupled Tensors.- Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection.- Unsupervised Domain Adaptation for 3D Medical Image with High Efficiency.- A Hierarchical Structure-Aware Embedding Method for Predicting Phenotype-Gene Associations.- Autonomous Vehicle Path Prediction using Conditional Variational Autoencoder Networks.- Heterogeneous Graph Attention Network for Small and Medium-sized Enterprises Bankruptcy Prediction.- Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.- Sim2Real for Metagenomes: Accelerating Animal Diagnostics with Adversarial Co-Training.- Attack Is the Best Defense: A Multi-Mode Poisoning PUF against Machine Learning Attacks.- Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets.- TLife-LSTM: Forecasting Future COVID-19 Progression with Topological Signatures of Atmospheric Conditions.- Lifelong Learning based Disease Diagnosis on Clinical Notes.- GrabQC: Graph based Query Contextualization for automated ICD coding.- Deep Gaussian Mixture Model on Multiple Interpretable Features of Fetal Heart Rate for Pregnancy Wellness.- Adverse Drug Events Detection, Extraction and Normalization from Online Comments of Chinese Patent Medicines.- Adaptive Graph Co-Attention Networks for Traffic Forecasting.- Dual-Stage Bayesian Sequence to Sequence Embeddings for Energy Demand Forecasting.- AA-LSTM: An Adversarial Autoencoder Joint Model for Prediction of Equipment Remaining Useful Life.- Data Mining of Specialized Data.- Analyzing Topic Transitions in Text-based Social Cascades using Dual-Network Hawkes Process.- HiPaR: Hierarchical Pattern-Aided Regression.- Improved Topology Extraction using Discriminative Parameter Mining of Logs.- Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion.- A k-MCST based Algorithm for Discovering Core-Periphery Structures in Graphs.- Detecting Sequentially Novel Classes with Stable Generalization Ability.- Learning-based Dynamic Graph Stream Sketch.- Discovering Dense Correlated Subgraphs in Dynamic Networks.- Fake News Detection with Heterogenous Deep Graph Convolutional Network.- Incrementally Finding the  Vertices Absent from the Maximum Independent Sets.- Neighbours and Kinsmen: HatefulUsers Detection with Graph Neural Network.- Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs.- A Meta-path based Graph Convolutional Network with Multi-Scale Semantic Extractions for Heterogeneous Event Classification.- Noise-Enhanced Unsupervised Link Prediction.- Weak Supervision Network Embedding for Constrained Graph Learning.- RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment               .- Graph Attention Networks with Positional Embeddings.- Unified Robust Training for Graph Neural Networks against Label Noise.- Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs.- A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention.- Maximizing Explainability with SF-Lasso and Selective Inference for Video and Picture Ads. -Reliably Calibrated Isotonic Regression.- Multiple Instance Learning for Unilateral Data.- An Online Learning Algorithm for Non-Stationary Imbalanced Data by Extra-Charging Minority Class.- Locally Linear Support Vector Machines for Imbalanced Data Classification. - Low-Dimensional Representation Learning from Imbalanced Data Streams.- PhotoStylist: Altering the Style of Photos based on the Connotations of Texts.- Gazetteer-Guided Keyphrase Generation from Research Papers.- Minits-AllOcc: An Efficient Algorithm for Mining Timed Sequential Patterns.- T^3N: Harnessing Text and Temporal Tree Network for Rumor Detection on Twitter.- AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection.- SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction.- Content matters: A GNN-based Model Combined with Text Semantics for Social Network Cascade Prediction.- TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting.- Traffic Flow Driven Spatio-Temporal Graph Convolutional Network for Ride-hailing Demand Forecasting.- A Proximity Forest for Multivariate Time Series Classification.- C²-Guard: A Cross-Correlation Gaining Framework for Urban Air Quality Prediction.- Simultaneous multiple POI population patternanalysis system with HDP mixture regression.- Interpretable Feature Construction for Time Series Extrinsic Regression.- SEPC: Improving Joint Extraction of Entities and Relations by Strengthening Entity Pairs Connection.