Atjaunināt sīkdatņu piekrišanu

E-grāmata: Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Revised Selected Papers, Part IV

Edited by , Edited by
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 89,21 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2023, Turin, Italy, September 18-22, 2023, Revised Selected Papers, Part IVPalielināt
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

The five-volume set CCIS 2133-2137 constitutes the refereed proceedings of the workshops held in conjunction with the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, during September 18-22, 2023.





The 200 full papers presented in these proceedings were carefully reviewed and selected from 515 submissions. The papers have been organized in the following tracks:





Part I: Advances in Interpretable Machine Learning and Artificial Intelligence -- Joint Workshop and Tutorial; BIAS 2023 - 3rd Workshop on Bias and Fairness in AI; Biased Data in Conversational Agents; Explainable Artificial Intelligence: From Static to Dynamic; ML, Law and Society; 





Part II: RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education; SoGood 2023 8th Workshop on Data Science for Social Good; Towards Hybrid Human-Machine Learning and Decision Making (HLDM); Uncertainty meets explainability in machine learning; Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation; 





Part III: XAI-TS: Explainable AI for Time Series: Advances and Applications; XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining; Deep Learning for Sustainable Precision Agriculture; Knowledge Guided Machine Learning; MACLEAN: MAChine Learning for EArth ObservatioN; MLG: Mining and Learning with Graphs; Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences; New Frontiers in Mining Complex Patterns; 





Part IV: PharML, Machine Learning for Pharma and Healthcare Applications; Simplification, Compression, Efficiency and Frugality for Artificial intelligence; Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making; 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL); Adapting to Change: Reliable Multimodal Learning Across Domains; AI4M: AI for Manufacturing; 





Part V: Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications; Deep learning meets Neuromorphic Hardware; Discovery challenge; ITEM: IoT, Edge, and Mobile for Embedded Machine Learning; LIMBO - LearnIng and Mining for BlOckchains; Machine Learning for Cybersecurity (MLCS 2023); MIDAS - The 8th Workshop on MIning DAta for financial applicationS; Workshop on Advancements in Federated Learning.
.- PharML, Machine Learning for Pharma and Healthcare Applications.



.- CORKI: A Correlation-driven Imputation Method for Partial Annotation
Scenarios in Multi-Label Clinical Problems.



.- Neuro-Symbolic Artificial Intelligence for Patient Monitoring.



.- Direct One-to-all Lead Conversion on 12-Lead Electrocardiogram.



.- Unveiling Driver Modules in Lung Cancer: A Clustering-Based Gene-Gene
Interaction Network Analysis.



.- Benchmarking Collaborative Learning Methods Cost-Effectiveness for
Prostate Segmentation.



.- Predicting Sepsis Onset with Deep Federated Learning.



.- A Workflow for Creating Multimodal Machine Learning Models for Metastasis
Predictions in Melanoma Patients.



.- Molecular Fingerprints-based Machine Learning.



.- Simplification, Compression, Efficiency and Frugality for Artificial
intelligence.



.- Neural Networks comprising Sequentially Semiseparable Matrices with one
dimensional State Variable are Universal Approximators.



.- TinyMetaFed: Efficient Federated Meta-Learning for TinyML.



.- On The Potentials of Input Repetition in CNN Networks for Reducing
Multiplications.



.- The Quest of Finding the Antidote to Sparse Double Descent.



.- Unveiling the Potential of Tiny Machine Learning for Enhanced People
Counting in UWB Radar Data.



.- Towards Comparable Knowledge Distillation in Semantic Image Segmentation.



.- Combining Primal and Dual Representations in Deep Restricted Kernel
Machines Classifiers.



.- Addressing limitations of TinyML approaches for AI-enabled Ambient
Intelligence (Position Paper).



.- Leveraging low rank filters for efficient and knowledge-preserving
lifelong learning.



.- Learning when to observe: A frugal reinforcement learning framework for a
high-cost world.



.- Workshop on Uplift Modeling and Causal Machine Learning for Operational
Decision Making.



.- Exploiting causal knowledge during CATE estimation using tree based
metalearners.



.- A Parameter-Free Bayesian Framework for Uplift Modeling - Application on
Telecom Data.



.- A churn prediction dataset from the telecom sector: a new benchmark for
uplift modeling.



.- 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted
Living (ARIAL) .



.- Semi-Supervised Co-Teaching for Monitoring Parkinson's Disease Patients.



.- Explainable Artificial Intelligence in Medical Diagnostics: Insights into
Alzheimer's Disease.



.- Cross-Modal Video to Body-joints Augmentation for Rehabilitation Exercise
Quality Assessment.



.- Multimodal Sensor Fusion for Daily Living Activities Recognition in Active
Assisted Living for Older Adults.



.- Modeling and Detecting Urinary Anomalies in Seniors from Data obtained by
Unintrusive Sensors.



.- Assessing Frailty Using Behavioral and Physical Health Data in Everyday
Living Settings.



.- Synthesizing Diabetic Foot Ulcer Images with Diffusion Model.



.- Engaging Older Adults at Meal-time through AI-empowered Socially Assistive
Robots.



.- Investigating the Dynamics of Cardio-metabolic Comorbidities and their
Interactions in Ageing Adults through Dynamic Bayesian Networks.



.- Adapting to Change: Reliable Multimodal Learning Across Domains.



.- Harnessing Error Patterns to Estimate Out-Of-Distribution Performance.



.- HAVE-Net: Hallucinated Audio-Visual Embeddings for Few-Shot Classification
with Unimodal Cues.



.- CAD Models to Real-World Images: A Practical Approach to Unsupervised
Domain Adaptation in Industrial Object Classification.



.- EMG subspace alignment and visualization for cross-subject hand gesture
classification.



.- Adapting Classifiers To Changing Class Priors During Deployment.



.- AI4M: AI for Manufacturing.



.- Applying Machine Learning Models on Metrology Data for Predicting Device
Electrical Performance.



.- Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply
Chains.



.- Reinforcement Learning for Segmented Manufacturing.



.- Automatic tool wear inspection by cascading sensor and image data.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97830317464066e.html