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_a10.1007/978-3-030-75768-7 _2doi |
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_aAdvances in Knowledge Discovery and Data Mining _h[electronic resource] : _b25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part III / _cedited by Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty. |
250 | _a1st ed. 2021. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021. |
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300 |
_aXXIII, 434 p. 142 illus., 117 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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490 | 1 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v12714 |
|
505 | 0 | _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. | |
520 | _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. | ||
650 | 0 |
_aArtificial intelligence. _93407 |
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650 | 0 |
_aSocial sciences _xData processing. _983360 |
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650 | 0 |
_aAlgorithms. _93390 |
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650 | 0 |
_aEducation _xData processing. _982607 |
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650 | 0 |
_aComputer science _xMathematics. _93866 |
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650 | 0 |
_aComputer vision. _9117657 |
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650 | 1 | 4 |
_aArtificial Intelligence. _93407 |
650 | 2 | 4 |
_aComputer Application in Social and Behavioral Sciences. _931815 |
650 | 2 | 4 |
_aDesign and Analysis of Algorithms. _931835 |
650 | 2 | 4 |
_aComputers and Education. _941129 |
650 | 2 | 4 |
_aMathematics of Computing. _931875 |
650 | 2 | 4 |
_aComputer Vision. _9117658 |
700 | 1 |
_aKarlapalem, Kamal. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117659 |
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700 | 1 |
_aCheng, Hong. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117660 |
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700 | 1 |
_aRamakrishnan, Naren. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117661 |
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700 | 1 |
_aAgrawal, R. K. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117662 |
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700 | 1 |
_aReddy, P. Krishna. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117663 |
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700 | 1 |
_aSrivastava, Jaideep. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117664 |
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700 | 1 |
_aChakraborty, Tanmoy. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9117665 |
|
710 | 2 |
_aSpringerLink (Online service) _9117666 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030757670 |
776 | 0 | 8 |
_iPrinted edition: _z9783030757694 |
830 | 0 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v12714 _9117667 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-75768-7 |
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