| # | Title | Journal | Year | Citations |
|---|
|
| 1 | Support-vector networks | Machine Learning | 1995 | 39,746 |
| 2 | Bagging predictors | Machine Learning | 1996 | 19,444 |
| 3 | Induction of decision trees | Machine Learning | 1986 | 12,466 |
| 4 | Gene Selection for Cancer Classification using Support Vector Machines | Machine Learning | 2002 | 7,471 |
| 5 | Induction of Decision Trees | Machine Learning | 1986 | 6,981 |
| 6 | Support-Vector Networks | Machine Learning | 1995 | 6,852 |
| 7 | Extremely randomized trees | Machine Learning | 2006 | 5,292 |
| 8 | Multitask Learning | Machine Learning | 1997 | 4,130 |
| 9 | Bayesian Network Classifiers | Machine Learning | 1997 | 3,732 |
| 10 | Simple statistical gradient-following algorithms for connectionist reinforcement learning | Machine Learning | 1992 | 3,579 |
| 11 | Bagging Predictors | Machine Learning | 1996 | 3,513 |
| 12 | Finite-time Analysis of the Multiarmed Bandit Problem | Machine Learning | 2002 | 3,502 |
| 13 | Instance-based learning algorithms | Machine Learning | 1991 | 3,335 |
| 14 | Learning to predict by the methods of temporal differences | Machine Learning | 1988 | 3,292 |
| 15 | The strength of weak learnability | Machine Learning | 1990 | 3,000 |
| 16 | Technical Note: Q-Learning | Machine Learning | 1992 | 2,603 |
| 17 | Support Vector Data Description | Machine Learning | 2004 | 2,579 |
| 18 | A Bayesian method for the induction of probabilistic networks from data | Machine Learning | 1992 | 2,550 |
| 19 | Theoretical and Empirical Analysis of ReliefF and RReliefF | Machine Learning | 2003 | 2,407 |
| 20 | Genetic Algorithms and Machine Learning | Machine Learning | 1988 | 2,380 |
| 21 | On the Optimality of the Simple Bayesian Classifier under Zero-One Loss | Machine Learning | 1997 | 2,368 |
| 22 | Learning Bayesian networks: The combination of knowledge and statistical data | Machine Learning | 1995 | 2,282 |
| 23 | (null) | Machine Learning | 2000 | 2,203 |
| 24 | Text Classification from Labeled and Unlabeled Documents using EM | Machine Learning | 2000 | 2,067 |
| 25 | Instance-Based Learning Algorithms | Machine Learning | 1991 | 2,065 |
| 26 | A theory of learning from different domains | Machine Learning | 2010 | 2,005 |
| 27 | An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants | Machine Learning | 1999 | 1,973 |
| 28 | An Introduction to Variational Methods for Graphical Models | Machine Learning | 1999 | 1,944 |
| 29 | Unsupervised Learning by Probabilistic Latent Semantic Analysis | Machine Learning | 2001 | 1,913 |
| 30 | Improved Boosting Algorithms Using Confidence-rated Predictions | Machine Learning | 1999 | 1,904 |
| 31 | Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy | Machine Learning | 2003 | 1,844 |
| 32 | Choosing Multiple Parameters for Support Vector Machines | Machine Learning | 2002 | 1,766 |
| 33 | Markov logic networks | Machine Learning | 2006 | 1,723 |
| 34 | BoosTexter: A Boosting-based System for Text Categorization | Machine Learning | 2000 | 1,700 |
| 35 | A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems | Machine Learning | 2001 | 1,688 |
| 36 | An Introduction to MCMC for Machine Learning | Machine Learning | 2003 | 1,673 |
| 37 | (null) | Machine Learning | 2003 | 1,670 |
| 38 | A Bayesian Method for the Induction of Probabilistic Networks from Data | Machine Learning | 1992 | 1,655 |
| 39 | The CN2 induction algorithm | Machine Learning | 1989 | 1,647 |
| 40 | Classifier chains for multi-label classification | Machine Learning | 2011 | 1,560 |
| 41 | The Strength of Weak Learnability | Machine Learning | 1990 | 1,534 |
| 42 | Very Simple Classification Rules Perform Well on Most Commonly Used Datasets | Machine Learning | 1993 | 1,445 |
| 43 | SPADE: An Efficient Algorithm for Mining Frequent Sequences | Machine Learning | 2001 | 1,432 |
| 44 | Knowledge acquisition via incremental conceptual clustering | Machine Learning | 1987 | 1,430 |
| 45 | A survey on semi-supervised learning | Machine Learning | 2020 | 1,410 |
| 46 | Learning in the presence of concept drift and hidden contexts | Machine Learning | 1996 | 1,378 |
| 47 | Genetic algorithms and Machine Learning | Machine Learning | 1988 | 1,377 |
| 48 | Queries and concept learning | Machine Learning | 1988 | 1,253 |
| 49 | The max-min hill-climbing Bayesian network structure learning algorithm | Machine Learning | 2006 | 1,206 |
| 50 | Stacked regressions | Machine Learning | 1996 | 1,203 |
