標(biāo)題: Titlebook: Machine Learning and Principles and Practice of Knowledge Discovery in Databases; International Worksh Irena Koprinska,Paolo Mignone,Sepide [打印本頁] 作者: GUST 時(shí)間: 2025-3-21 16:39
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書目名稱Machine Learning and Principles and Practice of Knowledge Discovery in Databases讀者反饋學(xué)科排名
作者: 遠(yuǎn)地點(diǎn) 時(shí)間: 2025-3-21 23:58 作者: Amorous 時(shí)間: 2025-3-22 01:10
Privacy-Preserving Machine Learning in?Life Insurance Risk Prediction One of the most relevant ones is privacy. However, privacy-preserving methods can potentially hinder the predictive potential of machine learning models. In this paper, we present preliminary experiments with life insurance data using two privacy-preserving techniques: discretization and encryption作者: artless 時(shí)間: 2025-3-22 06:57
Financial Distress Model Prediction Using Machine Learning: A Case Study on?Indonesia’s Consumers Cyal distress affects the sustainability of a company’s operations and undermines the rights and interests of its stakeholders, also harming the national economy and society. Therefore, we developed an accurate predictive model for financial distress. Using 17 financial attributes obtained from the fi作者: FLASK 時(shí)間: 2025-3-22 09:38 作者: 復(fù)習(xí) 時(shí)間: 2025-3-22 16:48
Towards Explainable Occupational Fraud Detectionenable automated detection of occupational fraud through recording large amounts of company data, the use of state-of-the-art machine learning approaches in this domain is limited by their untraceable decision process. In this study, we evaluate whether machine learning combined with explainable art作者: 懲罰 時(shí)間: 2025-3-22 18:19
Towards Data-Driven Volatility Modeling with?Variational Autoencodersautonomously learns concepts such as the volatility level, smile, and term structure without leaning on hypotheses from traditional volatility modeling techniques. In addition to introducing notable improvements to an existing variational autoencoder approach for the reconstruction of both complete 作者: 旁觀者 時(shí)間: 2025-3-23 01:02 作者: 施魔法 時(shí)間: 2025-3-23 05:19 作者: 侵略者 時(shí)間: 2025-3-23 08:18 作者: OFF 時(shí)間: 2025-3-23 12:05
Domain Adaptation with?Maximum Margin Criterion with?Application to?Network Traffic Classificationg-based network traffic classifier, it is necessary to use samples obtained from the desired network. Collecting enough training data, however, can be challenging in many cases. Domain adaptation allows samples from other networks to be utilized. In order to satisfy the aforementioned assumption, do作者: 精確 時(shí)間: 2025-3-23 15:33
Evaluation of?the?Limit of?Detection in?Network Dataset Quality Assessment with?PerQoDAetwork datasets that would enable effective detection. On the other hand, the preparation of a network dataset is not easy due to privacy reasons but also due to the lack of tools for assessing their quality. In a previous paper, we proposed a new method for data quality assessment based on permutat作者: 光滑 時(shí)間: 2025-3-23 21:20
Towards a General Model for Intrusion Detection: An Exploratory Studyires the expertise of the researchers, practitioners, or employees of companies that also have to gather labeled data to learn and evaluate the model that will then be deployed into a specific system. Reducing the expertise and time required to craft intrusion detectors is a tough challenge, which i作者: Sigmoidoscopy 時(shí)間: 2025-3-23 22:51
Conv-NILM-Net, a?Causal and?Multi-appliance Model for?Energy Source Separationral networks have become increasingly popular in attempting to solve NILM problems. However most used models are used for Load Identification rather than online Source Separation. Among source separation models, most use a single-task learning approach in which a neural network is trained exclusivel作者: 使隔離 時(shí)間: 2025-3-24 06:10 作者: 兇殘 時(shí)間: 2025-3-24 09:39
ting programs and models in terms of irreducible and independent tables. This idea departs from the mainstream of modeling & programming, which typically revolves around Application Program Interface (API) ecosystems for operational needs and external serialization for interchange needs. Instead, th作者: 混沌 時(shí)間: 2025-3-24 13:29 作者: ECG769 時(shí)間: 2025-3-24 16:07
Syrielle Montariol,Matej Martinc,Andra? Pelicon,Senja Pollak,Boshko Koloski,Igor Lon?arski,Aljo?a Vafundamental analysis problem of comparing a candidate implementation against a specification both given as TA, it has been shown that timed trace equivalence is undecidable, whereas timed bisimulation is decidable. However, the limited expressiveness of TA is a serious obstacle in practice such that作者: 追逐 時(shí)間: 2025-3-24 22:38
Argimiro Arratiaprocesses, and tools to develop software from scratch. In reality, however, greenfield scenarios are not the most common ones. It is important to realize that dynamic evolution of software became a much more common and relevant issue in recent times, and its importance keeps growing. Software refact作者: 使更活躍 時(shí)間: 2025-3-25 00:50 作者: Dungeon 時(shí)間: 2025-3-25 04:59
Klismam Pereira,Jo?o Vinagre,Ana Nunes Alonso,Fábio Coelho,Melania Carvalhobeen introduced, to reduce power consumption at the expense of performance. We consider DPM (Dynamic Power Management) and DVFS (Dynamic Voltage and Frequency Scaling). The complex programming task now includes mapping and scheduling every task onto a heterogeneous multi-processor hardware platform.作者: Hiatus 時(shí)間: 2025-3-25 09:27 作者: INERT 時(shí)間: 2025-3-25 12:21
Stefano Piersantively lead to their advancement. This is also the case for failure detection and scheduling component replacements. The large number of factors that influence how failures occur during operation of a CPS may result in maintenance policies that are time-monitoring based, which can lead to suboptimal s作者: Dorsal 時(shí)間: 2025-3-25 19:13 作者: Polydipsia 時(shí)間: 2025-3-25 23:18
Thomas Dierckx,Jesse Davis,Wim Schoutensethods, ISoLA 2020, which was planned to take place during October 20–30, 2020, on Rhodes, Greece. The event itself was postponed to 2021 due to the COVID-19 pandemic...The papers presented were carefully reviewed and selected for inclusion in the proceedings. ..Each volume focusses on an individual作者: Cpr951 時(shí)間: 2025-3-26 00:47 作者: encomiast 時(shí)間: 2025-3-26 07:48 作者: 藥物 時(shí)間: 2025-3-26 09:28 作者: 珠寶 時(shí)間: 2025-3-26 13:48 作者: tackle 時(shí)間: 2025-3-26 17:37 作者: Dignant 時(shí)間: 2025-3-26 21:34
Katarzyna Wasielewska,Dominik Soukup,Tomá? ?ejka,José Camachonments. An increase in capabilities and thus complexity consequently led to a dramatic increase in possible faults that might manifest in errors. Even worse, by applying robots with emerging behavior in non-deterministic real-world environments, faults may be introduced from external sources. Conseq作者: 用手捏 時(shí)間: 2025-3-27 04:52 作者: 震驚 時(shí)間: 2025-3-27 08:27 作者: transplantation 時(shí)間: 2025-3-27 10:49 作者: 吹牛需要藝術(shù) 時(shí)間: 2025-3-27 14:44 作者: Apraxia 時(shí)間: 2025-3-27 20:04 作者: 令人發(fā)膩 時(shí)間: 2025-3-28 00:07 作者: 相容 時(shí)間: 2025-3-28 04:06 作者: omnibus 時(shí)間: 2025-3-28 10:07 作者: Inflammation 時(shí)間: 2025-3-28 12:16
Conv-NILM-Net, a?Causal and?Multi-appliance Model for?Energy Source Separationration, we propose Conv-NILM-net, a fully convolutional framework for end-to-end NILM. Conv-NILM-net is a causal model for multi appliance source separation. Our model is tested on two real datasets REDD and UK-DALE and clearly outperforms the state of the art while keeping a significantly smaller size than the competing models.作者: Synovial-Fluid 時(shí)間: 2025-3-28 17:15
Privacy-Preserving Machine Learning in?Life Insurance Risk Predictionm in three general, but plausible Use Cases involving the prediction of insurance claims within a 1-year horizon. Our preliminary experiments suggest that discretization and encryption have negligible impact in the accuracy of ML models.作者: Gossamer 時(shí)間: 2025-3-28 20:06
Financial Distress Model Prediction Using Machine Learning: A Case Study on?Indonesia’s Consumers Cytree, logistic regression, LightGBM, and the k-nearest neighbor algorithms. The overall accuracy of the proposed model ranged from 0.60 to 0.87, which improved on using the one-year prior growth data of financial attributes.作者: Promotion 時(shí)間: 2025-3-29 00:30 作者: flamboyant 時(shí)間: 2025-3-29 05:49
Conference proceedings 2023earning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, held in Grenoble, France, during September 19–23, 2022.?.The 73 revised full papers and 6 short papers presented in this book were carefully reviewed and selected from 143 submissions. ECML PKDD 2022 presents th作者: 極微小 時(shí)間: 2025-3-29 09:26
1865-0929 Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, held in Grenoble, France, during September 19–23, 2022.?.The 73 revised full papers and 6 short papers presented in this book were carefully reviewed and selected from 143 submissions. ECML PKDD 2022 p作者: BOON 時(shí)間: 2025-3-29 13:58 作者: fluoroscopy 時(shí)間: 2025-3-29 17:19 作者: chassis 時(shí)間: 2025-3-29 21:47 作者: 廣口瓶 時(shí)間: 2025-3-30 00:09
https://doi.org/10.1007/978-3-031-23633-4artificial intelligence; classification methods; computer crime; computer networks; computer security; co作者: 碳水化合物 時(shí)間: 2025-3-30 05:04
978-3-031-23632-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Celiac-Plexus 時(shí)間: 2025-3-30 09:38 作者: 低能兒 時(shí)間: 2025-3-30 12:32 作者: 和平主義 時(shí)間: 2025-3-30 19:03 作者: 治愈 時(shí)間: 2025-3-30 22:48
Intrusion Detection Using Ensemble Models use pairs of strong and weak learners based on five different classifiers and combine them using weights derived through a Particle Swarm Optimization algorithm. We propose a voting and a stacking scheme to obtain the final predictions. We show the overwhelming advantage of using our proposed stack作者: Frequency 時(shí)間: 2025-3-31 03:52
Domain Adaptation with?Maximum Margin Criterion with?Application to?Network Traffic Classificationabeled samples from the desired network; In other words, we adapt shared applications while preserving the information about non-shared applications. In order to demonstrate the efficacy of our method, we construct five different cross-network datasets using the Brazil dataset. These results indicat作者: 招待 時(shí)間: 2025-3-31 08:24
Towards a General Model for Intrusion Detection: An Exploratory Studyme them. Then, we perform an experimental evaluation using several binary ML classifiers and a total of 16 feature learners on 4 public attack datasets. Results show that a model learned on a dataset or a system does not generalize well as is to other datasets or systems, showing poor detection perf作者: CODA 時(shí)間: 2025-3-31 10:53
Domestic Hot Water Forecasting for?Individual Housing with?Deep Learning achieved satisfying performances in term of MSE on an individual residence dataset, showing that this approach is promising to conceive building energy management systems based on deep forecasting models.
