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06933nam a22006615i 4500 |
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978-3-030-75768-7 |
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DE-He213 |
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20210625093735.0 |
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210507s2021 gw | s |||| 0|eng d |
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|a 9783030757687
|9 978-3-030-75768-7
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|a 10.1007/978-3-030-75768-7
|2 doi
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|a Q334-342
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|a Advances in Knowledge Discovery and Data Mining
|h [electronic resource] :
|b 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part III /
|c edited by Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty.
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| 250 |
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|a 1st ed. 2021.
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| 264 |
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2021.
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| 300 |
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|a XXIII, 434 p. 142 illus., 117 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a Lecture Notes in Artificial Intelligence ;
|v 12714
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| 505 |
0 |
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|a 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.
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|a 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.
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| 650 |
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0 |
|a Artificial intelligence.
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| 650 |
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0 |
|a Application software.
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| 650 |
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|a Algorithms.
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| 650 |
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0 |
|a Education—Data processing.
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| 650 |
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0 |
|a Computer science—Mathematics.
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| 650 |
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0 |
|a Optical data processing.
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| 650 |
1 |
4 |
|a Artificial Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I21000
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| 650 |
2 |
4 |
|a Computer Appl. in Social and Behavioral Sciences.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I23028
|
| 650 |
2 |
4 |
|a Algorithm Analysis and Problem Complexity.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I16021
|
| 650 |
2 |
4 |
|a Computers and Education.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I24032
|
| 650 |
2 |
4 |
|a Mathematics of Computing.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I17001
|
| 650 |
2 |
4 |
|a Image Processing and Computer Vision.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I22021
|
| 700 |
1 |
|
|a Karlapalem, Kamal.
|e editor.
|0 (orcid)0000-0003-2528-7979
|1 https://orcid.org/0000-0003-2528-7979
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
|
|a Cheng, Hong.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
|
|a Ramakrishnan, Naren.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Agrawal, R. K.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
|
|a Reddy, P. Krishna.
|e editor.
|0 (orcid)0000-0003-1238-5174
|1 https://orcid.org/0000-0003-1238-5174
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 700 |
1 |
|
|a Srivastava, Jaideep.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 700 |
1 |
|
|a Chakraborty, Tanmoy.
|e editor.
|0 (orcid)0000-0002-0210-0369
|1 https://orcid.org/0000-0002-0210-0369
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
|
| 710 |
2 |
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|a SpringerLink (Online service)
|
| 773 |
0 |
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|t Springer Nature eBook
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9783030757670
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783030757694
|
| 830 |
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0 |
|a Lecture Notes in Artificial Intelligence ;
|v 12714
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-030-75768-7
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| 912 |
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|a ZDB-2-SCS
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| 912 |
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|a ZDB-2-SXCS
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| 912 |
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|a ZDB-2-LNC
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| 950 |
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|a Computer Science (SpringerNature-11645)
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| 950 |
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|a Computer Science (R0) (SpringerNature-43710)
|
