会议信息

MLDM 2025: International Conference on Machine Learning and Data Mining

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MLDM
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截稿日期:
2025-02-15
通知日期:
2025-03-20
会议日期:
2025-07-18
会议地点:
Dresden, Germany
届数:
20
QUALIS: B2   浏览: 45009   关注: 31   参加: 3

会伴指数 (CP-I)

59.8 / 100
全站第 612 名 / 共 5,682 个会议 · 前 11%

数据挖掘与数据库 第 32 / 337 人工智能与机器学习 第 50 / 739

学术认可 (35%)
68
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
82
社区关注 (10%)
44
资料公开度 (15%)
35

用到的输入: 收录等级：QUALIS B2 · 有据可查的届次：20 · 在会伴关注它的研究者：31 人 · 过去 24 个月打开过本页的研究者：3 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 80% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-18

征稿

MLDM 2025 (International Conference on Machine Learning and Data Mining) is a QUALIS B2 conference held in Dresden, Germany on 2025-07-18. The paper submission deadline is 2025-02-15. Acceptance notifications are sent on 2025-03-20.

The Aim of the Conference The aim of the conference is to bring together researchers from all over the world who deal with machine learning and data mining in order to discuss the recent status of the research and to direct further developments. Basic research papers as well as application papers are welcome. Topics of the conference All kinds of applications are welcome but special preference will be given to multimedia related applications, applications from live sciences and webmining. Paper submissions should be related but not limited to any of the following topics: association rules case-based reasoning and learning classification and interpretation of images, text, video conceptional learning and clustering Goodness measures and evaluaion (e.g. false discovery rates) inductive learning including decision tree and rule induction learning knowledge extraction from text, video, signals and images mining gene data bases and biological data bases mining images, temporal-spatial data, images from remote sensing mining structural representations such as log files, text documents and HTML documents mining text documents organisational learning and evolutional learning probabilistic information retrieval Sampling methods Selection with small samples similarity measures and learning of similarity statistical learning and neural net based learning video mining visualization and data mining Applications of Clustering Aspects of Data Mining Applications in Medicine Autoamtic Semantic Annotation of Media Content Bayesian Models and Methods Case-Based Reasoning and Associative Memory Classification and Model Estimation Content-Based Image Retrieval Decision Trees Deviation and Novelty Detection Feature Grouping, Discretization, Selection and Transformation Feature Learning Frequent Pattern Mining High-Content Analysis of Microscopic Images in Medicine, Biotechnology and Chemistry Learning and adaptive control Learning/adaption of recognition and perception Learning for Handwriting Recognition Learning in Image Pre-Processing and Segmentation Learning in process automation Learning of internal representations and models Learning of appropriate behaviour Learning of action patterns Learning of Ontologies Learning of Semantic Inferencing Rules Learning of Visual Ontologies Learning robots Mining Images in Computer Vision Mining Images and Texture Mining Motion from Sequence Neural Methods Network Analysis and Intrusion Detection Nonlinear Function Learning and Neural Net Based Learning Real-Time Event Learning and Detection Retrieval Methods Rule Induction and Grammars Speech Analysis Statistical and Conceptual Clustering Methods Statistical and Evolutionary Learning Subspace Methods Support Vector Machines Symbolic Learning and Neural Networks in Document Processing Time Series and Sequential Pattern Mining Audio Mining Cognition and Computer Vision Clustering Classification & Prediction Statistical Learning Association Rules Telecommunication Design of Experiment Strategy of Experimentation Capability Indices Deviation and Novelty Detection Control Charts Design of Experiments Capability Indices Conceptional Learning Goodness Measures and Evaluation (e.g. false discovery rates) Inductive Learning Including Decision Tree and Rule Induction Learning Organisational Learning and Evolutional Learning Sampling Methods Similarity Measures and Learning of Similarity Statistical Learning and Neural Net Based Learning Visualization and Data Mining Deviation and Novelty Detection Feature Grouping, Discretization, Selection and Transformation Feature Learning Frequent Pattern Mining Learning and Adaptive Control Learning/Adaption of Recognition and Perception Learning for Handwriting Recognition Learning in Image Pre-Processing and Segmentation Mining Financial or Stockmarket Data Mining Motion from Sequence Subspace Methods Support Vector Machines Time Series and Sequential Pattern Mining Desirabilities Graph Mining Agent Data Mining Applications in Software Testing
由 Dou Sun 最后更新于

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