LEADER 06020nam 22007335 450 001 9910483761903321 005 20251113180652.0 010 $a3-030-75768-4 024 7 $a10.1007/978-3-030-75768-7 035 $a(CKB)4100000011918704 035 $a(DE-He213)978-3-030-75768-7 035 $a(MiAaPQ)EBC6607522 035 $a(Au-PeEL)EBL6607522 035 $a(OCoLC)1250275933 035 $a(PPN)255881452 035 $a(EXLCZ)994100000011918704 100 $a20210507d2021 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Knowledge Discovery and Data Mining $e25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11?14, 2021, Proceedings, Part III /$fedited by Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (XXIII, 434 p. 142 illus., 117 illus. in color.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v12714 311 08$a3-030-75767-6 327 $aRepresentation 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. 330 $aThe 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v12714 606 $aArtificial intelligence 606 $aSocial sciences$xData processing 606 $aAlgorithms 606 $aEducation$xData processing 606 $aComputer science$xMathematics 606 $aComputer vision 606 $aArtificial Intelligence 606 $aComputer Application in Social and Behavioral Sciences 606 $aDesign and Analysis of Algorithms 606 $aComputers and Education 606 $aMathematics of Computing 606 $aComputer Vision 615 0$aArtificial intelligence. 615 0$aSocial sciences$xData processing. 615 0$aAlgorithms. 615 0$aEducation$xData processing. 615 0$aComputer science$xMathematics. 615 0$aComputer vision. 615 14$aArtificial Intelligence. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aDesign and Analysis of Algorithms. 615 24$aComputers and Education. 615 24$aMathematics of Computing. 615 24$aComputer Vision. 676 $a006.3 702 $aKarlapalem$b Kamal 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483761903321 996 $aAdvances in Knowledge Discovery and Data Mining$9772012 997 $aUNINA