Machine learning and principles and ...
ECML PKDD (Conference) ((2022 :)

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  • Machine learning and principles and practice of knowledge discovery in databases = International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022 : proceedings.. Part II /
  • 紀錄類型: 書目-電子資源 : Monograph/item
    正題名/作者: Machine learning and principles and practice of knowledge discovery in databases/ edited by Irena Koprinska ... [et al.].
    其他題名: International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022 : proceedings.
    其他題名: ECML PKDD 2022
    其他作者: Koprinska, Irena.
    團體作者: ECML PKDD (Conference)
    出版者: Cham :Springer Nature Switzerland : : 2023.,
    面頁冊數: xix, 487 p. :ill., digital ;24 cm.
    內容註: Workshop on Mining Data for Financial Application (MIDAS 2022) -- Preface from the workshop organisers -- Multi-Task Learning for Features Extraction in Financial Annual Reports -- What to do with your sentiments in finance -- On the development of a European tracker of societal issues and economic activities using alternative data -- Privacy-preserving machine learning in life insurance risk prediction -- Financial Distress Model Prediction using Machine Learning: A Case Study on Indonesia's Consumers Cyclical Companies -- Improve default prediction in highly unbalanced context -- Towards Explainable Occupational Fraud Detection -- Towards Data-Driven Volatility Modeling with Variational Autoencoders -- Auto-Clustering of Financial Reports Based on Formatting Style and Author's Fingerprint -- InFi-BERT 1.0: Transformer-based language model for Indian Financial Volatility Prediction -- Workshop on Machine Learning for Cybersecurity (MLCS 2022) -- Preface from the workshop organisers -- Intrusion Detection using Ensemble Models -- Domain Adaptation with Maximum Margin Criterion with application to network traffic classification -- Evaluation of Detection Limit in Network Dataset Quality Assessment with Permutation Testing -- Towards a General Model for Intrusion Detection: An Exploratory Study -- Workshop on Machine Learning for Buildings Energy Management (MLBEM 2022) -- Preface from the workshop organisers -- Conv-NILM-Net, a causal and multi-appliance model for energy source separation -- Domestic Hot Water Forecasting for Individual Housing with Deep Learning -- Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2022) -- Preface from the workshop organisers -- Detecting Drift in Healthcare AI Models based on Data Availability -- Assessing Different Feature Selection Methods applied to a bulk RNA Sequencing Dataset with regard to Biomedical Relevance -- Predicting Drug Treatment for Hospitalized Patients with Heart Failure -- A Workflow for Generating Patient Counterfactuals in Lung Transplant Recipients -- Few-Shot Learning for Identification of COVID-19 Symptoms Using Generative Pre-Trained Transformer Language Models -- A Light-weight Deep Residual Network for Classification of Abnormal Heart Rhythms on Tiny Devices -- Workshop on Data Analysis in Life Science (DALS 2022) -- Preface from the workshop organisers -- I-CONVEX: Fast and Accurate de Novo Transcriptome Recovery from Long Reads -- Italian debate on measles vaccination: how Twitter data highlight communities and polarity -- Workshop on IoT Streams for Predictive Maintenance (IoT-PdM 2022) -- Preface from the workshop organisers -- Online Anomaly Explanation: A Case Study on Predictive Maintenance -- Fault forecasting using data-driven system modeling: a case study for Metro do Porto data set -- An online data-driven predictive maintenance approach for railway switches -- curr2vib: Modality Embedding Translation for Broken-Rotor Bar Detection -- Incorporating Physics-based Models into Data-Driven Approaches for Air Leak Detection in City Buses -- Towards Geometry-Preserving Domain Adaptation for Fault Identification -- A systematic approach for tracking the evolution of XAI as a field of research -- Frequent Generalized Subgraph Mining via Graph Edit Distances.
    Contained By: Springer Nature eBook
    標題: Machine learning - Congresses. -
    電子資源: https://doi.org/10.1007/978-3-031-23633-4
    ISBN: 9783031236334
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