Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track; European Conference, Albert Bifet,Jesse Davis,Indrė Žliobaitė Confer

STRI

书目名称Machine Learning and Knowledge Discovery in Databases. Research Track影响因子(影响力)




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track影响因子(影响力)学科排名




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track网络公开度




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track网络公开度学科排名




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track被引频次




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track被引频次学科排名




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track年度引用




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track年度引用学科排名




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track读者反馈




书目名称Machine Learning and Knowledge Discovery in Databases. Research Track读者反馈学科排名

光滑

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620544.jpg

SLAY

CRUE

SPURN

Fast Redescription Mining Using Locality-Sensitive Hashing we present new algorithms that perform the matching and extension orders of magnitude faster than the existing approaches. Our algorithms are based on locality-sensitive hashing with a tailored approach to handle the discretisation of numerical attributes as used in redescription mining.

predict

figure

Conference proceedings 2024scovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024... ..The papers presented in these proceedings are from the following three conference tracks: -..Research Track:. The 202 full papers presented here, from this track, were carefully reviewed and selected from 826 su

LAIR

emulsify

Model-Based Reinforcement Learning with Multi-task Offline Pretrainingsferring the task-agnostic knowledge of physical dynamics to facilitate world model training, and (ii) learning to replay relevant source actions to guide the target policy. We demonstrate the advantages of our approach compared with the state-of-the-art methods in Meta-World and DeepMind Control Suite.

LIMN