Produktbild: Data Science: Foundations and Applications
Band 15875

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 VI

87,99 €

inkl. gesetzl. MwSt., Versandkostenfrei


Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.06.2025

Herausgeber

Xintao Wu + weitere

Verlag

Springer Singapore

Seitenzahl

473

Maße (L/B/H)

23,5/15,5/2,8 cm

Gewicht

756 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9682-94-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.06.2025

Herausgeber

Verlag

Springer Singapore

Seitenzahl

473

Maße (L/B/H)

23,5/15,5/2,8 cm

Gewicht

756 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9682-94-2

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)

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

    .- Large Language Models for Cybersecurity Education: A Survey of Current Practices and Future Directions.

    .- A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping.

    .- A Survey of Foundation Models for Environmental Science.

    .- A Survey on Efficient Graph Reachability Queries.

    .- Machine Learning.

    .- Disentangled Representation Learning for Geospatial-temporal Data Modeling.

    .- Treatment Effect Estimation for Graph-Structured Targets.

    .- Dynamic DropConnect: Enhancing Neural Network Robustness through Adaptive Edge Dropping Strategies.

    .- The Brownian Integral Kernel: A New Kernel for Modeling Integrated Brownian Motions.

    .- Fed-ARIMA-OPARBFN: An Ensemble Model for Cross-Domain Crop Yield Time Series Prediction Based on Federated Learning.

    .- S-CPD: Topological Smoothing-Based Change Point Detection.

    .- VDASI: VAE-Enhanced Degradation-Aware System Identification Using Constrained Latent Spaces.

    .- Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning.

    .- PFformer: A Position-Free Transformer Variant for Extreme-Adaptive Multivariate Time Series Forecasting.

    .- Advancing Long-Term High-Frequency Dissolved Oxygen Forecasting for Australian Rivers.

    .- CNO-former: Chaotic Neural Oscillatory Transformer for Social Media Text Generation.

    .- Multilingual Non-Factoid Question Answering with Answer Paragraph Selection

    .- Turning Uncertainty to Information by Intervals in Ensemble Classifiers.

    .- Determining the Need for Multi-Label Classifiers by Measuring Unexplained Covariance.

    .- Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics.

    .- Trustworthiness.

    .- Inversion Triplet - A Contrastive Backdoor Mitigation Method for Self-Supervised Vision Encoders.

    .- Beyond Uniformity: Robust Backdoor Attacks on Deep Neural Networks with Trigger Selection.

    .- Defence Against Multi-target Multi-trigger Backdoor Attack.

    .- How to Backdoor Consistency Models?.

    .- Multi-granularity Policy Explanation of Deep Reinforcement Learning Based on Saliency Map Clustering.

    .- FACROC: A Fairness Measure for Fair Clustering Through ROC Curves.

    .- Learning on Complex Data.

    .- Action Sequence Analysis Using Temporal Commonsense Knowledge.

    .- Foundation Model for Lossy Compression of Spatiotemporal Scientific Data.

    .- CANTER: A Novel Causal Model for Tourism Demand Forecasting.

    .- Time-Aware Complex Attention Space for Temporal Knowledge Graph Completion.

    .- Adaptive Extraction of Variable-Length Subsequence Patterns in Noisy Time Series.

    .- Hunting Inside N-Quantiles of Outliers (Hino).

    .- Fast Approximation Algorithm for Euclidean Minimum Spanning Tree Building in High Dimensions.

    .- ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton.

    .- Offline Map Matching Based on Localization Error Distribution Modeling.