Machine Learning SCIESCI

Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems, including but not limited to:

Learning Problems: Classification, regression, recognition, and prediction; Problem solving and planning; Reasoning and inference; Data mining; Web mining; Scientific discovery; Information retrieval; Natural language processing; Design and diagnosis; Vision and speech perception; Robotics and control; Combinatorial optimization; Game playing; Industrial, financial, and scientific applications of all kinds.

Learning Methods: Supervised and unsupervised learning methods (including learning decision and regression trees, rules, connectionist networks, probabilistic networks and other statistical models, inductive logic programming, case-based methods, ensemble methods, clustering, etc.); Reinforcement learning; Evolution-based methods; Explanation-based learning; Analogical learning methods; Automated knowledge acquisition; Learning from instruction; Visualization of patterns in data; Learning in integrated architectures; Multistrategy learning; Multi-agent learning.

机器学习是一个研究计算学习方法的国际论坛。该期刊发表的文章报告了适用于各种学习问题的各种学习方法的实质性成果，包括但不限于：

学习问题：分类、回归、识别和预测；解决问题和计划；推理和推理；数据挖掘;网络挖掘；科学发现；信息检索；自然语言处理；设计与诊断；视觉和言语感知；机器人与控制；组合优化；玩游戏；各种工业、金融和科学应用。

学习方法：有监督和无监督的学习方法（包括学习决策和回归树、规则、连接主义网络、概率网络和其他统计模型、归纳逻辑编程、基于案例的方法、集成方法、聚类等）；强化学习；基于进化的方法；基于解释的学习；类比学习方法；自动化知识获取；从指导中学习；数据模式的可视化；在集成架构中学习；多策略学习；多智能体学习。

该刊已被国际权威数据库SCIE、SCI收录，是国际顶级期刊。该刊致力于发表经过严格同行评审的高质量原创文章，反映工程技术-计算机：人工智能领域的新进展、新技术、新成果，促进该领域科研交流和科研成果转化。该刊2022年影响因子为7.5，平均审稿速度为较慢,6-12周，近三年来没有被列入预警名单。如果您需要投稿发表服务及指导，可以联系我们的客服老师，我们专业专注服务期刊投稿协助10年，为您提供期刊投稿个性化定制服务，并且我们确保严格保密您的个人信息及稿件内容。