找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track; European Conference, Yuxiao Dong,Dunja Mladenić,Craig Sa

[复制链接]
楼主: Retina
发表于 2025-3-25 07:05:51 | 显示全部楼层
发表于 2025-3-25 07:30:44 | 显示全部楼层
Mingxuan Yue,Tianshu Sun,Fan Wu,Lixia Wu,Yinghui Xu,Cyrus Shahabi
发表于 2025-3-25 12:36:14 | 显示全部楼层
Social Influence Attentive Neural Network for Friend-Enhanced Recommendationd to capture the influence of the friend referral circle in an attentive manner. Experimental results demonstrate that SIAN outperforms several state-of-the-art baselines on three real-world datasets. (Code and dataset are available at .).
发表于 2025-3-25 18:24:27 | 显示全部楼层
发表于 2025-3-25 20:25:02 | 显示全部楼层
Strategic and Crowd-Aware Itinerary Recommendationation as Markov chains which enables our simulations to be carried out in poly-time. We then evaluate our proposed algorithm against various competitive and realistic baselines using a theme park dataset. Our simulation results highlight the existence of the Selfish Routing problem and show that SCA
发表于 2025-3-26 01:15:28 | 显示全部楼层
A Context-Aware Approach to Detect Abnormal Human Behaviors ACtivity ONtology (HACON), is proposed to conceptualize the contexts of human behaviors. Finally, a probabilistic version of ASP, a high-level expressive logic-based formalism, is proposed to detect abnormal behaviors through a set of rules based on the HACON ontology. The proposed approach is eval
发表于 2025-3-26 06:18:32 | 显示全部楼层
RADAR: Recurrent Autoencoder Based Detector for Adversarial Examples on Temporal EHR show that RADAR can filter out more than 90% of adversarial examples and improve the target model accuracy by more than . and F1 score by 60%. Besides, we also propose an enhanced attack by introducing the distribution divergence into the loss function such that the adversarial examples are more re
发表于 2025-3-26 10:20:08 | 显示全部楼层
发表于 2025-3-26 13:49:23 | 显示全部楼层
Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysists indicate that our system incorporating a nonparametric survival analysis technique called ‘Random Survival Forest’ outperforms several popular algorithms and expert heuristics in long-term prediction. In addition, we construct a statistical inference technique to quantify the uncertainty associat
发表于 2025-3-26 19:53:40 | 显示全部楼层
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-7-3 14:02
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表