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学习的计算方法的国际论坛。该杂志发表的文章报告了广泛的学习方法应用于各种学习问题的实质性结果，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; Web挖掘;科学发现;情报检索;自然语言处理设计和诊断;视觉和言语知觉;机器人与控制组合优化;游戏;工业、金融和科学的各种应用。2学习方法：监督和非监督学习方法（包括学习决策和回归树、规则、连接网络、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;类比学习法;自动化知识获取;从指导中学习;数据模式的可视化综合体系结构中的学习多策略学习;多智能体学习。
Gradient descent optimizes over-parameterized deep ReLU networks
来源期刊：Machine LearningDOI：10.1007/s10994-019-05839-6
A survey on semi-supervised learning
来源期刊：Machine LearningDOI：10.1007/s10994-019-05855-6
Accelerated gradient boosting
来源期刊：Machine LearningDOI：10.1007/s10994-019-05787-1
Kappa Updated Ensemble for drifting data stream mining
来源期刊：Machine LearningDOI：10.1007/s10994-019-05840-z
Efficient learning with robust gradient descent
来源期刊：Machine LearningDOI：10.1007/s10994-019-05802-5
Engineering fast multilevel support vector machines
来源期刊：Machine LearningDOI：10.1007/s10994-019-05800-7
Learning higher-order logic programs
来源期刊：Machine LearningDOI：10.1007/s10994-019-05862-7
Distributed block-diagonal approximation methods for regularized empirical risk minimization
来源期刊：Machine LearningDOI：10.1007/s10994-019-05859-2
A Riemannian gossip approach to subspace learning on Grassmann manifold
来源期刊：Machine LearningDOI：10.1007/s10994-018-05775-x
Logical reduction of metarules
来源期刊：Machine LearningDOI：10.1007/s10994-019-05834-x
Handling concept drift via model reuse
来源期刊：Machine LearningDOI：10.1007/s10994-019-05835-w
Improved graph-based SFA: information preservation complements the slowness principle
来源期刊：Machine LearningDOI：10.1007/s10994-019-05860-9
Grouped Gaussian processes for solar power prediction
来源期刊：Machine LearningDOI：10.1007/s10994-019-05808-z
Aggregating Algorithm for prediction of packs
来源期刊：Machine LearningDOI：10.1007/s10994-018-5769-2
Compatible natural gradient policy search
来源期刊：Machine LearningDOI：10.1007/s10994-019-05807-0
Exploiting causality in gene network reconstruction based on graph embedding
来源期刊：Machine LearningDOI：10.1007/s10994-019-05861-8
The teaching size: computable teachers and learners for universal languages
来源期刊：Machine LearningDOI：10.1007/s10994-019-05821-2
Sum–product graphical models
来源期刊：Machine LearningDOI：10.1007/s10994-019-05813-2
Combining Bayesian optimization and Lipschitz optimization
来源期刊：Machine LearningDOI：10.1007/s10994-019-05833-y
On PAC-Bayesian bounds for random forests
来源期刊：Machine LearningDOI：10.1007/s10994-019-05803-4
Analysis of Hannan consistent selection for Monte Carlo tree search in simultaneous move games
来源期刊：Machine LearningDOI：10.1007/s10994-019-05832-z
A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data
来源期刊：Machine LearningDOI：10.1007/s10994-019-05810-5
RankMerging: a supervised learning-to-rank framework to predict links in large social networks
来源期刊：Machine LearningDOI：10.1007/s10994-019-05792-4
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric
来源期刊：Machine LearningDOI：10.1007/s10994-019-05836-9
Efficient feature selection using shrinkage estimators
来源期刊：Machine LearningDOI：10.1007/s10994-019-05795-1
Skill-based curiosity for intrinsically motivated reinforcement learning
来源期刊：Machine LearningDOI：10.1007/s10994-019-05845-8
Rank minimization on tensor ring: an efficient approach for tensor decomposition and completion
来源期刊：Machine LearningDOI：10.1007/s10994-019-05846-7
Annotation cost-sensitive active learning by tree sampling
来源期刊：Machine LearningDOI：10.1007/s10994-019-05781-7
The kernel Kalman rule
来源期刊：Machine LearningDOI：10.1007/s10994-019-05816-z
Joint consensus and diversity for multi-view semi-supervised classification
来源期刊：Machine LearningDOI：10.1007/s10994-019-05844-9
Application of Queuing Theory to a Bank’s Automated Teller Machine (ATM) Service Optimization
来源期刊：Machine LearningDOI：10.11648/J.ML.20190501.12
Dynamic principal projection for cost-sensitive online multi-label classification
来源期刊：Machine LearningDOI：10.1007/s10994-018-5773-6
Speculate-correct error bounds for k-nearest neighbor classifiers
来源期刊：Machine LearningDOI：10.1007/s10994-019-05814-1
Asymptotically optimal algorithms for budgeted multiple play bandits
来源期刊：Machine LearningDOI：10.1007/s10994-019-05799-x
On some graph-based two-sample tests for high dimension, low sample size data
来源期刊：Machine LearningDOI：10.1007/s10994-019-05857-4
A bad arm existence checking problem: How to utilize asymmetric problem structure?
来源期刊：Machine LearningDOI：10.1007/s10994-019-05854-7
Predictive spreadsheet autocompletion with constraints
来源期刊：Machine LearningDOI：10.1007/s10994-019-05841-y
A flexible probabilistic framework for large-margin mixture of experts
来源期刊：Machine LearningDOI：10.1007/s10994-019-05811-4
Online Bayesian max-margin subspace learning for multi-view classification and regression
来源期刊：Machine LearningDOI：10.1007/s10994-019-05853-8
PERAN MODAL SOSIAL KETUA KELOMPOK TANI DAN DAMPAKNYA TERHADAP KNOWLEDGE SHARING PETANI STUDI PADA PETANI DI KABUPATEN ENREKANG
来源期刊：Machine LearningDOI：10.35891/ML.V10I2.1446
PENINGKATAN NILAI PERUSAHAAN MELALUI TATA KELOLA PERUSAHAAN DAN PROFITABILITAS PADA KONSTITUEN INDEKS SAHAM SYARIAH INDONESIA
来源期刊：Machine LearningDOI：10.35891/ML.V10I2.1428
LSALSA: accelerated source separation via learned sparse coding
来源期刊：Machine LearningDOI：10.1007/s10994-019-05812-3
A distributed feature selection scheme with partial information sharing
来源期刊：Machine LearningDOI：10.1007/s10994-019-05809-y
Improving latent variable descriptiveness by modelling rather than ad-hoc factors
来源期刊：Machine LearningDOI：10.1007/s10994-019-05830-1
Joint detection of malicious domains and infected clients
来源期刊：Machine LearningDOI：10.1007/s10994-019-05789-z
2D compressed learning: support matrix machine with bilinear random projections
来源期刊：Machine LearningDOI：10.1007/s10994-019-05804-3
The COMPARISON OF SHARIA BANKING PERFORMANCE BASED ON RISK PROFILE AND EARNING AND CAPITAL (REC)
来源期刊：Machine LearningDOI：10.35891/ml.v11i1.1729
Introduction to the special issue for the ECML PKDD 2019 journal track
来源期刊：Machine LearningDOI：10.1007/s10994-019-05831-0
Preface to special issue on Inductive Logic Programming, ILP 2017 and 2018
来源期刊：Machine LearningDOI：10.1007/s10994-019-05790-6
