Machine Learning

Journal Title：Machine Learning

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.

机器学习是研究计算学习方法的国际论坛。该期刊发表的文章报告了应用于各种学习问题的广泛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；问题解决和规划；推理和推论；数据挖掘；网络挖掘；科学发现；信息检索；自然语言处理；设计和诊断；视觉和语音感知；机器人和控制；组合优化；游戏；各种工业、金融和科学应用。

学习方法：监督和无监督学习方法（包括学习决策和回归树、规则、联结网络、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；自动知识获取；从指令中学习；数据模式可视化；集成架构中的学习；多策略学习；多智能体学习。

Machine Learning创刊于1986年，由Springer US出版商出版，收稿方向涵盖工程技术 - 计算机：人工智能全领域，此刊是中等级别的SCI期刊，所以过审相对来讲不是特别难，但是该刊专业认可度不错，仍然是一本值得选择的SCI期刊 。平均审稿速度较慢,6-12周，影响因子指数4.9，该期刊近期没有被列入国际期刊预警名单，广大学者值得一试。