Machine Learning
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In The Last Decade
Machine Learning
2.3k papers receiving 285.9k citations
Fields of papers published in Machine Learning
This network shows the impact of papers published in Machine Learning. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Machine Learning.
Countries where authors publish in Machine Learning
This map shows the geographic impact of research published in Machine Learning. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Machine Learning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Machine Learning more than expected).
- Random Forests (2001)
- Support-vector networks (1995)
- Support-Vector Networks (1995)
- Bagging predictors (1996)
- Bagging Predictors (1996)
- Induction of decision trees (1986)
- Induction of Decision Trees (1986)
- Gene Selection for Cancer Classification using Support Vector Machines (2002)
- Q-learning (1992)
- Extremely randomized trees (2006)
- Simple statistical gradient-following algorithms for connectionist reinforcement learning (1992)
- Multitask Learning (1997)
- Bayesian Network Classifiers (1997)
- Finite-time Analysis of the Multiarmed Bandit Problem (2002)
- Instance-Based Learning Algorithms (1991)
- Genetic Algorithms and Machine Learning (1988)
- Theoretical and Empirical Analysis of ReliefF and RReliefF (2003)
- Technical Note: Q-Learning (1992)
- Support Vector Data Description (2003)
- A Bayesian Method for the Induction of Probabilistic Networks from Data (1992)
- Instance-based learning algorithms (1991)
- Learning to Predict by the Methods of Temporal Differences (1988)
- An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization (2000)
- The Strength of Weak Learnability (1990)
- An Introduction to Variational Methods for Graphical Models (1999)
- Learning Bayesian Networks: The Combination of Knowledge and Statistical Data (1995)
- On the Optimality of the Simple Bayesian Classifier under Zero-One Loss (1997)
- A theory of learning from different domains (2009)
- The strength of weak learnability (1990)
- Learning to predict by the methods of temporal differences (1988)
- A survey on semi-supervised learning (2019)
- A Bayesian method for the induction of probabilistic networks from data (1992)
- A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems (2001)
- Learning Bayesian networks: The combination of knowledge and statistical data (1995)
- Classifier chains for multi-label classification (2011)
- Improved Boosting Algorithms Using Confidence-rated Predictions (1999)
- Self-improving reactive agents based on reinforcement learning, planning and teaching (1992)
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar’s output or impact.