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Data science = foundations and applications : 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025 : proceedings.. Part VII /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data science/ edited by Xintao Wu ... [et al.].
Reminder of title:	foundations and applications : 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025 : proceedings.
remainder title:	PAKDD 2025
other author:	Wu, Xintao.
corporate name:	Workshop on the Preservation of Stability under Discretization
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xxx, 441 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Data mining - Congresses. -
Online resource:	https://doi.org/10.1007/978-981-96-8298-0
ISBN:	9789819682980

Data science = foundations and applications : 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025 : proceedings.. Part VII /
Data sciencefoundations and applications : 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025 : proceedings.Part VII /[electronic resource] :PAKDD 2025edited by Xintao Wu ... [et al.]. - Singapore :Springer Nature Singapore :2025. - xxx, 441 p. :ill. (chiefly color), digital ;24 cm. - Lecture notes in computer science,158761611-3349 ;. - Lecture notes in computer science ;6140..

Graph Mining. -- MuCo-KGC: Multi-Context-Aware Knowledge Graph Completion. -- Tensor-Fused Multi-View Graph Contrastive Learning. -- FOG: Interpretable Feature-Oriented Graph Neural Networks for Tabular Data Prediction. -- High Resolution Image Classification with Rich Text Information Based on Graph Convolution Neural Network. -- Time Interval Aware Graph Neural Networks for Session-Based Recommendation. -- SSGNN: Structure-aware Scoring Graph Neural Network for Molecular Representation. -- Mint: An Efficient and Robust In-Place Update Approach for Graph-based Vector Index. -- Machine Learning Applications. -- Advancing Comprehensive Aspect-Based Sentiment Analysis with Generative Models. -- A Systematic Evaluation of Generative Models on Tabular Transportation Data. -- SDF-Guided Multi-modal Big Data Road Extraction. -- Player Movement Predictions Using Team and Opponent Dynamics for Doubles Badminton. -- Representation Learning. -- Late Fusion Ensembles for Speech Recognition on Diverse Input Audio Representations. -- Text Enhancement-based Multimodal Fusion for Video Sentiment Analysis. -- Advancing Rubric-based Automated Essay Scoring with Multi-View BERT: A Case Study in New Zealand. -- A Script Event Prediction Method Based on Multi-Level Joint Pretraining and Prompt Fine-Tuning. -- Scientific/Business Data Analysis. -- A Multimodal Fusion Model Leveraging MLP Mixer and Handcrafted Features-based Deep Learning Networks for Facial Palsy Detection. -- Using Pseudo-Synonyms to Generate Embeddings for Clinical Terms. -- Corporate Carbon Emission Prediction: Combining Structured and Unstructured Data. -- GDCK: Efficient Large-Scale Graph Distillation utilizing a Model-free Kernelized Approach. -- Efficient DNA fragment assembly based on Discrete Slime Mould Algorithm. -- Multi-Scale Control Model for Network Group Behavior. -- Can Self Supervision Rejuvenate Similarity-Based Link Prediction?. -- Managing Data Uncertainty in Automatic Mapping of Clinical Classification Systems. -- Insomnia Detection Based on Brain State Sleep Trajectories. -- MCA: Multimodal Contrastive Augmentation for Medical Report Generation. -- Special Track on Large Language Models. -- Adapting Large Language Models for Parameter-Efficient Log Anomaly Detection. -- Bot Wars Evolved: Orchestrating Competing LLMs in a Counterstrike Against Phone Scams. -- Large Language Models with Multi-Faceted Relation Alignment for User Novel Interest Discovery. -- Estimating Impact of Behavior Change Messages Using Large Language Models. -- A Meta-Thinking Approach to Mitigating Linguistic Sycophancy in Vision-Language Models. -- VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models. -- TRAWL: Tensor Reduced and Approximated Weights for Large Language Models. -- DAG-Think-Twice: Causal Structure Guided Elicitation of Causal Reasoning in Large Language Model. -- GRL-Prompt: Towards Prompts Optimization via Graph-empowered Reinforcement Learning using LLMs' Feedback.

The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10-13, 2025. The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.

ISBN: 9789819682980

Standard No.: 10.1007/978-981-96-8298-0doiSubjects--Topical Terms:

528575
Data mining
--Congresses.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Data science = foundations and applications : 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025 : proceedings.. Part VII /
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