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Advances in Knowledge Discovery and Data Mining

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The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discov... Weiterlesen
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Beschreibung

The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021.

The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows:

Part I: Applications of knowledge discovery and data mining of specialized data;

Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics;

Part III: Representation learning and embedding, and learning from data.



Inhalt

Classical Data Mining,. Mining Frequent Patterns from Hypergraph Databases.- Discriminating Frequent Pattern based Supervised Graph Embedding for Classification.- Mining Sequential Patterns in Uncertain Databases Using Hierarchical Index Structure.- Similarity Forest Revisited: a Swiss Army Knife for Machine Learning.- Discriminative Representation Learning for Cross-domain Sentiment Classification.- SAGCN: Towards Structure-Aware Deep Graph Convolutional Networks on Node Classification.- Hierarchical Learning of Dependent Concepts for Human Activity Recognition.- Improving Short Text Classification Using Context-Sensitive Representations and Content-Aware Extended Topic Knowledge.- A Novel Method for Offline Handwritten Chinese Character Recognition under the Guidance of Print.- Upgraded Attention-based Local FeatureLearning Block for speech emotion recognition.- Memorization in Deep Neural Networks: Does the Loss Function matter.- Gaussian Soft Decision Trees for Interpretable Feature-Based Classification.- Efficient Nodes Representation Learning with Residual Feature Propagation.- Progressive AutoSpeech: An efficient and general framework for automatic speech classification.- CrowdTeacher: Robust Co-teaching with Noisy Answers & Sample-specific Perturbations for Tabular Data.- Effective and Adaptive Multi-metric Refined Similarity Graph Fusion for Multi-view Clustering.- aHCQ: Adaptive Hierarchical Clustering based Quantization Framework for Deep Neural Networks.- Maintaining Consistency with Constraints: a Constrained Deep Clustering method.- Data Mining Theory and Principles.- Towards multi-label Feature selection by Instance and Label Selections.- FARF: A Fair and Adaptive Random Forests Classifier.- Sparse Spectrum Gaussian Process for Bayesian Optimization.- Densely Connected Graph Attention Network based on Iterative Path Reasoning for Document-level Relation Extraction.- Causal Inference Using Global Forecasting Models for Counterfactual Prediction. -CED-BGFN: Chinese Event Detection via Bidirectional Glyph-aware Dynamic Fusion Network.- Learning Finite Automata with Shuffle.- Active Learning based Similarity Filtering for Efficient and Effective Record Linkage.- Stratified Sampling for Extreme Multi-Label Data.- Vertical Federated Learning for Higher-order Factorization Machines.- dK-Projection: Publishing Graph Joint degree distribution with Node Differential Privacy.- Recommender Systems.- Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks.- Exploring Implicit Relationships in Social Network for Recommendation Systems.- Transferable Contextual Bandits with Prior Observations.- Modeling Hierarchical Intents and Selective Current Interest for Session-based Recommendation.- A Finetuned language model for Recommending cQA-QAs for enriching Textbooks.- XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction.- Learning Multiclass Classifier Under Noisy Bandit Feedback.- Diversify or Not: Dynamic Diversification for Personalized Recommendation.- Multi-criteria and Review-based Overall Rating Prediction.- W2FM: The Doubly-Warped Factorization Machine.- Causal Combinatorial Factorization Machines for Set-wise Recommendation.- Transformer-based Multi-task Learning for Queuing Time Aware Next POI Recommendation.- Joint Modeling Dynamic Preferences of Users and Items Using Reviews for Sequential Recommendation.- Box4Rec: Box Embedding for Sequential Recommendation.- UKIRF: An Item Rejection Framework for Improving Negative Items Sampling in One-Class Collaborative Filtering.- IACN: Influence-aware and Attention-based Co-evolutionary Network for Recommendation.- Nonlinear Matrix Factorization via Neighbor Embedding.- Deconfounding representation learning based on user interactions in Recommendation Systems.- Personalized Regularization Learning for Fairer Matrix Factorization.- Instance Selection for Online Updating in Dynamic Recommender Environments.- Text Analytics.- Fusing Essential Knowledge for Text-Based Open-Domain Question Answering. - TSSE-DMM: Topic Modeling for Short Texts based on Topic Subdivision and Semantic Enhancement.- SILVER: Generating Persuasive Chinese Product Pitch.- Capturing SQL Query Overlapping via SubtreeCopy for Cross-domain Context-dependent SQLGeneration.- HScodeNet: Combining Hierarchical Sequential and Global Spatial Information of Text for Commodity HS Code Classification.- PLVCG: A Pretraining Based Model for Live Video Comment Generation.- Inducing Rich Interaction Structures between Words for Document-level Event Argument Extraction.- Exploiting Relevant Hyperlinks in Knowledge Base for Entity Linking.- TANTP: Conversational Emotion Recognition Using Tree-Based Attention Networks with Transformer Pre-training.- Semantic-syntax Cascade Injection Model for Aspect Sentiment Triple Extraction.- Modeling Inter-Aspect Relationship with Conjunction for Aspect-based Sentiment Analysis.

Produktinformationen

Titel: Advances in Knowledge Discovery and Data Mining
Untertitel: 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part II
Editor:
EAN: 9783030757656
Format: E-Book (pdf)
Hersteller: Springer International Publishing
Genre: IT & Internet
Veröffentlichung: 07.05.2021
Digitaler Kopierschutz: Wasserzeichen
Dateigrösse: 55.07 MB
Anzahl Seiten: 774

Weitere Bände aus der Buchreihe "Lecture Notes in Artificial Intelligence"