Información de la conferencia

MLDM 2025: International Conference on Machine Learning and Data Mining

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MLDM
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Día de Entrega:
2025-02-15
Fecha de Notificación:
2025-03-20
Fecha de conferencia:
2025-07-18
Ubicación:
Dresden, Germany
Ediciones:
20
QUALIS: B2   Vistas: 45011   Seguidores: 31   Asistentes: 3

Índice Conference Partner (CP-I)

59,8 / 100
Puesto n.º 612 de 5.682 congresos · 11% superior

N.º 32 de 337 en Minería de datos y bases de datos N.º 50 de 739 en Inteligencia artificial y aprendizaje automático

Reconocimiento académico (35%)
68
Selectividad en la revisión (20%) Sin datos: se puntúa con la línea base neutra de 50 —
Ediciones celebradas (20%)
82
Atención de la comunidad (10%)
44
Integridad del registro público (15%)
35

Datos utilizados: Categorías: QUALIS B2 · Ediciones documentadas: 20 · Investigadores que lo siguen aquí: 31 · Investigadores que abrieron esta página en los últimos 24 meses: 3

Falta en el registro público: Tasas de aceptación históricas (+4,5) · Ediciones anteriores (+3,0) · Premios al mejor artículo (+2,3)
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Confianza 80 %: la parte de la puntuación respaldada por datos observados y no por la línea base neutra. Cómo se calcula esta puntuación · Ver la clasificación · Versión del algoritmo 1.1 · Calculado el 2026-09-18

Solicitud de Artículos

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
Última actualización por Dou Sun el

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