# | Title | Journal | Year | Citations |
---|
1 | Support-vector networks | Machine Learning | 1995 | 38,107 |
2 | Bagging predictors | Machine Learning | 1996 | 18,759 |
3 | Induction of decision trees | Machine Learning | 1986 | 12,034 |
4 | Q-learning | Machine Learning | 1992 | 8,093 |
5 | Gene Selection for Cancer Classification using Support Vector Machines | Machine Learning | 2002 | 7,229 |
6 | Induction of Decision Trees | Machine Learning | 1986 | 6,920 |
7 | Support-Vector Networks | Machine Learning | 1995 | 6,650 |
8 | Extremely randomized trees | Machine Learning | 2006 | 4,796 |
9 | Bayesian Network Classifiers | Machine Learning | 1997 | 3,662 |
10 | Bagging Predictors | Machine Learning | 1996 | 3,456 |
11 | Finite-time Analysis of the Multiarmed Bandit Problem | Machine Learning | 2002 | 3,350 |
12 | Simple statistical gradient-following algorithms for connectionist reinforcement learning | Machine Learning | 1992 | 3,328 |
13 | Instance-based learning algorithms | Machine Learning | 1991 | 3,271 |
14 | Learning to predict by the methods of temporal differences | Machine Learning | 1988 | 3,207 |
15 | The strength of weak learnability | Machine Learning | 1990 | 2,922 |
16 | Technical Note: Q-Learning | Machine Learning | 1992 | 2,549 |
17 | A Bayesian method for the induction of probabilistic networks from data | Machine Learning | 1992 | 2,512 |
18 | Support Vector Data Description | Machine Learning | 2004 | 2,482 |
19 | Genetic Algorithms and Machine Learning | Machine Learning | 1988 | 2,316 |
20 | Theoretical and Empirical Analysis of ReliefF and RReliefF | Machine Learning | 2003 | 2,316 |
21 | Learning Bayesian networks: The combination of knowledge and statistical data | Machine Learning | 1995 | 2,244 |
22 | Instance-Based Learning Algorithms | Machine Learning | 1991 | 2,061 |
23 | Text Classification from Labeled and Unlabeled Documents using EM | Machine Learning | 2000 | 2,050 |
24 | An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants | Machine Learning | 1999 | 1,936 |
25 | An Introduction to Variational Methods for Graphical Models | Machine Learning | 1999 | 1,889 |
26 | Improved Boosting Algorithms Using Confidence-rated Predictions | Machine Learning | 1999 | 1,885 |
27 | Unsupervised Learning by Probabilistic Latent Semantic Analysis | Machine Learning | 2001 | 1,884 |
28 | A theory of learning from different domains | Machine Learning | 2010 | 1,852 |
29 | Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy | Machine Learning | 2003 | 1,814 |
30 | Choosing Multiple Parameters for Support Vector Machines | Machine Learning | 2002 | 1,746 |
31 | Markov logic networks | Machine Learning | 2006 | 1,700 |
32 | BoosTexter: A Boosting-based System for Text Categorization | Machine Learning | 2000 | 1,674 |
33 | A Bayesian Method for the Induction of Probabilistic Networks from Data | Machine Learning | 1992 | 1,649 |
34 | An Introduction to MCMC for Machine Learning | Machine Learning | 2003 | 1,641 |
35 | The CN2 induction algorithm | Machine Learning | 1989 | 1,627 |
36 | (null) | Machine Learning | 2003 | 1,613 |
37 | The Strength of Weak Learnability | Machine Learning | 1990 | 1,527 |
38 | Classifier chains for multi-label classification | Machine Learning | 2011 | 1,483 |
39 | Knowledge acquisition via incremental conceptual clustering | Machine Learning | 1987 | 1,423 |
40 | Very Simple Classification Rules Perform Well on Most Commonly Used Datasets | Machine Learning | 1993 | 1,417 |
41 | SPADE: An Efficient Algorithm for Mining Frequent Sequences | Machine Learning | 2001 | 1,411 |
42 | Learning in the presence of concept drift and hidden contexts | Machine Learning | 1996 | 1,364 |
43 | Genetic algorithms and Machine Learning | Machine Learning | 1988 | 1,357 |
44 | Queries and concept learning | Machine Learning | 1988 | 1,230 |
45 | A survey on semi-supervised learning | Machine Learning | 2020 | 1,224 |
46 | Stacked regressions | Machine Learning | 1996 | 1,152 |
47 | Learning logical definitions from relations | Machine Learning | 1990 | 1,151 |
48 | The max-min hill-climbing Bayesian network structure learning algorithm | Machine Learning | 2006 | 1,145 |
49 | Soft Margins for AdaBoost | Machine Learning | 2001 | 1,000 |
50 | Machine Learning for the Detection of Oil Spills in Satellite Radar Images | Machine Learning | 1998 | 986 |