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.

機器學習是研究計算學習方法的國際論壇。該期刊發表的文章報告了應用于各種學習問題的廣泛學習方法的實質性成果，包括但不限于：

學習問題：分類、回歸、識別和預測；問題解決和規劃；推理和推論；數據挖掘；網絡挖掘；科學發現；信息檢索；自然語言處理；設計和診斷；視覺和語音感知；機器人和控制；組合優化；游戲；各種工業、金融和科學應用。

學習方法：監督和無監督學習方法（包括學習決策和回歸樹、規則、聯結網絡、概率網絡和其他統計模型、歸納邏輯編程、基于案例的方法、集成方法、聚類等）；強化學習；基于進化的方法；基于解釋的學習；類比學習方法；自動知識獲取；從指令中學習；數據模式可視化；集成架構中的學習；多策略學習；多智能體學習。

Machine Learning創刊于1986年，由Springer US出版商出版，收稿方向涵蓋工程技術 - 計算機：人工智能全領域，此刊是中等級別的SCI期刊，所以過審相對來講不是特別難，但是該刊專業認可度不錯，仍然是一本值得選擇的SCI期刊 。平均審稿速度 較慢,6-12周 ，影響因子指數4.3，該期刊近期沒有被列入國際期刊預警名單，廣大學者值得一試。