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Titlebook: Machine Learning and Principles and Practice of Knowledge Discovery in Databases; International Worksh Michael Kamp,Irena Koprinska,Guilher

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發(fā)表于 2025-3-21 18:11:43 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Machine Learning and Principles and Practice of Knowledge Discovery in Databases
副標(biāo)題International Worksh
編輯Michael Kamp,Irena Koprinska,Guilherme Gra?a
視頻videohttp://file.papertrans.cn/621/620574/620574.mp4
叢書(shū)名稱Communications in Computer and Information Science
圖書(shū)封面Titlebook: Machine Learning and Principles and Practice of Knowledge Discovery in Databases; International Worksh Michael Kamp,Irena Koprinska,Guilher
描述This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online.?.The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops.?This two-volume set includes the proceedings of the following workshops:.Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021).Workshop on Parallel, Distributed and Federated Learning (PDFL 2021).Workshop on Graph Embedding and Mining ?(GEM 2021).Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021).Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021).Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021).Workshop on Bias and Fairness in AI (BIAS 2021).Workshop on Workshop on Active Inference (IWAI 2021).Workshop on Machine Learning for Cybersecurity (MLCS 2021).Workshop on Machine Learning in Software Engineering (MLiSE 2021).Workshop on MIning Data for financial applications (MIDAS 2021).Sixth Wor
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; computational linguistics; computer hardware; computer networks; computer secur
版次1
doihttps://doi.org/10.1007/978-3-030-93733-1
isbn_softcover978-3-030-93732-4
isbn_ebook978-3-030-93733-1Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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978-3-030-93732-4Springer Nature Switzerland AG 2021
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Machine Learning and Principles and Practice of Knowledge Discovery in Databases978-3-030-93733-1Series ISSN 1865-0929 Series E-ISSN 1865-0937
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發(fā)表于 2025-3-22 07:11:50 | 只看該作者
A Reasoning Approach to Financial Data Exchange with Statistical ConfidentialityMotivated by our experience with the Bank of Italy, in this work we present Vada-SA, a reasoning framework for financial data exchange with statistical confidentiality. By reasoning on the interplay of the features that may lead to identity disclosure, the framework is able to guarantee explainable, declarative, and context-aware confidentiality.
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On Neural Forecasting and?News Emotions: The Case of?the?Spanish Stock Marketloyed to produce point and density forecasts. After providing an overview of the methodology under current development, some preliminary findings are also given, showing an improvement in the IBEX-35 index fitting when the emotional variables are included in the model, and an overall validity of the employed approach.
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Linguistic Analysis of?Stack Overflow Data: Native English vs Non-native English Speakerss related to software development. We analyze the communications related to software collaboration using linguistic features. We mine data from online question-answer forum, . and evaluate machine learning approaches to classify contributors as native English speakers . non-native English speakers.
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Financial Forecasting with?Word Embeddings Extracted from?News: A Preliminary Analysisle forecasting the US S&P500 index by means of DeepAR, an advanced neural forecasting method based on auto-regressive Recurrent Neural Networks operating in a probabilistic setting. Although this is currently on-going work, the obtained preliminary results look promising, suggesting an overall validity of the employed methodology.
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