Produktbild: Data Science: Foundations and Applications
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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

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.06.2025

Herausgeber

Xintao Wu + weitere

Verlag

Springer Singapore

Seitenzahl

441

Maße (L/B/H)

23,5/15,5/2,6 cm

Gewicht

709 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9682-97-3

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.06.2025

Herausgeber

Verlag

Springer Singapore

Seitenzahl

441

Maße (L/B/H)

23,5/15,5/2,6 cm

Gewicht

709 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9682-97-3

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Data Science: Foundations and Applications
  • .- 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.