会议信息

ICMLA 2026: International Conference on Machine Learning and Applications

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ICMLA
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截稿日期:
2026-05-15
通知日期:
2026-06-15
会议日期:
2026-10-05
会议地点:
Rochester, Michigan, USA
届数:
25
ICORE: C   QUALIS: B2   浏览: 47426   关注: 44   参加: 12

会伴指数 (CP-I)

62.4 / 100
全站第 528 名 / 共 5,693 个会议 · 前 10%

人工智能与机器学习 第 41 / 742

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%)
88
社区关注 (10%)
58
资料公开度 (15%)
35

用到的输入: 收录等级：ICORE C, QUALIS B2 · 有据可查的届次：25 · 在会伴关注它的研究者：44 人 · 过去 24 个月打开过本页的研究者：16 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 2026-10-05

征稿

ICMLA 2026 (International Conference on Machine Learning and Applications) is a ICORE C / QUALIS B2 conference held in Rochester, Michigan, USA on 2026-10-05. The paper submission deadline is 2026-05-15. Acceptance notifications are sent on 2026-06-15.

ICMLA 2026 aims to bring together researchers and practitioners to present their latest achievements and innovations in the area of machine learning (ML). The conference provides a leading international forum for the dissemination of original research in ML, with emphasis on applications as well as novel algorithms and systems. Following the success of previous ICMLA conferences, the conference aims to attract researchers and application developers from a wide range of ML related areas, and the recent emergence of Big Data processing brings an urgent need for machine learning to address these new challenges. The conference will cover both machine learning theoretical research and its applications. Contributions describing machine learning techniques applied to real-world problems and interdisciplinary research involving machine learning, in fields like medicine, biology, industry, manufacturing, security, education, virtual environments,games, are especially encouraged. Contributions describing applications of machine learning (ML)techniques to real-world problems, interdisciplinary research involving machine learning, experimental and/or theoretical studies yielding new insights into the design of ML systems, and papers describing development of new analytical frameworks that advance practical machine learning methods are especially encouraged. The technical program will consist of, but is not limited to, the following topics of interest: statistical learning neural network learning learning through fuzzy logic learning through evolution (evolutionary algorithms) reinforcement learning multi-strategy learning cooperative learning planning and learning multi-agent learning online and incremental learning scalability of learning algorithms inductive learning inductive logic programming Bayesian networks support vector machines case-based reasoning machine learning for bioinformatics and computational biology multi-lingual knowledge acquisition and representation grammatical inference knowledge acquisition and learning knowledge discovery in databases knowledge intensive learning knowledge representation and reasoning machine learning and information retrieval machine learning for web navigation and mining learning through mobile data mining text and multimedia mining through machine learning distributed and parallel learning algorithms and applications feature extraction and classification theories and models for plausible reasoning computational learning theory cognitive modeling deep and transfer learning federated learning machine learning on the edge machine learning for computer vision hybrid learning algorithms Applications of machine learning in: medicine, health, bioinformatics and systems biology industrial and engineering applications security applications smart cities and autonomous driving game playing and problem solving intelligent virtual environments economics, business and forecasting applications, etc.
由 Dou Sun 最后更新于

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