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 II

Edited by , Edited by
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 95,16 €*
  • * 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 IIPalielināt
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.
.- RKDE 2023: 1st International Tutorial and Workshop on Responsible
Knowledge Discovery in Education.



.- PICA: A Data-driven Synthesis of Peer Instruction and Continuous
Assessment.



.- The ChatGPT and Education Tweets Dataset.



.- A Fair Post-Processing Method based on the MADD Metric for Predictive
Student Models.



.- Distractor generation for multiple-choice questions with predictive
prompting and large language models.



.- Towards Personalized Educational Materials: Mapping Student Knowledge
through Natural Language Processing.



.-  A 2-step methodology for XAI in education.



.- Consolidation and Transmission of Multiple xAPI Data Sources from Virtual
Learning Environments to Different Learning Record Stores .



.- SoGood 2023 8th Workshop on Data Science for Social Good.



.- Efficient and general text classification: An Active Learning approach.



.- Identifying Features of Constructive Journalism in News Articles: An
Explainable ML Approach.



.- Anomaly Detection in Pet Behavioral Data.



.- Detecting sexually explicit content in the context of the child sexual
abuse materials (CSAM): end-to-end classifiers and region-based networks.



.- PrivateCTGAN: Adapting GAN for Privacy-aware Tabular Data Sharing.



.- Data Science for Fighting Environmental Crime.



.- Fairness Analysis in Causal Models: An Application to Public Procurement.



.- Exploring the Generalizability of Transfer Learning for Camera Trap Animal
Image Classification.



.- Towards Hybrid Human-Machine Learning and Decision Making (HLDM).



.- Towards a hybrid human-machine discovery of complex movement patterns.



.- Trustworthy Hybrid Decision Making.



.- Optimizing delegation between human and AI collaborative agents.



.- Exploring the Risks of General-Purpose AI: The Role of Nearsighted Goals
and the Brain's Reward Mechanism in Processes of Decision Makings.



.- Towards synergistic human-AI collaboration in hybrid decision-making
systems.



.- On the Challenges and Practices of Reinforcement Learning from Real Human
Feedback.



.- Conversational XAI: Formalizing its Basic Design Principles.



.- TCuPGAN: A novel framework developed for optimizing human-machine
interactions in citizen science.



.- A Crossroads for Hybrid Human-Machine decision-making.



.- Enhancing Fairness, Justice and Accuracy of Hybrid Human AI Decisions by
Shifting Epistemological Stances.



.- Interpreting Dynamic Causal Model Policies.



.- Uncertainty meets explainability in machine learning.



.- Relation of Activity and Confidence when Training Deep Neural Networks.



.- Explaining an image classifier with a GAN conditioned by uncertainty.



.- Identifying Trends in Feature Attributions during Training of Neural
Networks.



.- Using Stochastic Methods to Setup High Precision Experiments.



.- Designing a Method to Identify Explainability Requirements in Cancer
Research.



.- Explainable Learning with Hierarchical Online Deterministic Annealing.



.- Explaining uncertainty in AI for clinical decision support systems.



.- Towards Explainability in Monocular Depth Estimation.



.- Using Part-based Representations for Explainable Deep Reinforcement
Learning.



.- Regionally Additive Models: Explainable-by-design models minimizing
feature interactions.



.- FALE: Fairness aware ALE plots for auditing bias in subgroups.



.- Workshop: Deep Learning and Multimedia Forensics. Combating fake media and
misinformation.



.- Tracing Videos to their Social Network with Robust DCT Analysis.



.- All-for-One and One-For-All: Deep learning-based feature fusion for
Synthetic Speech Detection.



.- Improving Tiled Evolutionary Adversarial Attack.



.- Adversarial Magnification to Deceive Deepfake Detection through Super
Resolution.



.- DivNoise: A Data Collection for Source Identification on Diverse Camera
Sensors.



.- Detecting Face Synthesis Using a Concealed Fusion Model.



.- Adversarial Data Poisoning for Fake News Detection: How to Make a Model
Misclassify a Target News without Modifying It.



.- Towards a Fine-Grained Threat Model for Video-Based Remote Identity
Proofing.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97830317462776e.html