The Strength of Weak Learnability

Cited by (1,455)

Items 501–600 of 1,455

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2. Ore Grade Prediction Using a Genetic Algorithm and Clustering Based Ensemble Neural Network Model
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3. Quantum ensembles of quantum classifiers
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4. Visual features based boosted classification of weeds for real-time selective herbicide sprayer systems
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5. Performance evaluation of boosting machine learning algorithms for lithofacies classification in heterogeneous carbonate reservoirs
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6. Ensemble Classifiers and Their Applications: A Review
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7. An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes
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8. Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm
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9. Feature selection in single and ensemble learning‐based bankruptcy prediction models
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10. Genetic ensemble of extreme learning machine
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11. A Novel Hierarchical-Clustering-Combination Scheme Based on Fuzzy-Similarity Relations
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12. Ensemble methods and partial least squares regression
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13. Additive models, boosting, and inference for generalized divergences
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14. Strong Machine Learning Attack Against PUFs with No Mathematical Model
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15. Robust Statistical Label Fusion Through Consensus Level, Labeler Accuracy, and Truth Estimation (COLLATE)
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16. Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data
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17. Pruning an ensemble of classifiers via reinforcement learning
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18. A survey of adaptive resonance theory neural network models for engineering applications
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19. An improved combination approach based on Adaboost algorithm for wind speed time series forecasting
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20. Neural network ensembles: combining multiple models for enhanced performance using a multistage approach
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21. NARX-Based Nonlinear System Identification Using Orthogonal Least Squares Basis Hunting
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22. A case study on bagging, boosting and basic ensembles of neural networks for OCR
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23. An Overview and Empirical Comparison of Distance Metric Learning Methods
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24. Kolmogorov's spline network
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25. Intrusion detection of multiple attack classes using a deep neural net ensemble
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26. Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer's disease
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27. Data Analytics for Non-Life Insurance Pricing
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28. DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT
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29. The Case for Being Automatic: Introducing the Automatic Linear Modeling (LINEAR) Procedure in SPSS Statistics
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30. Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering
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31. Statistical Learning in Palaeolimnology
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32. On the value of parameter tuning in heterogeneous ensembles effort estimation
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33. Key agreement from weak bit agreement
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34. GIR-based ensemble sampling approaches for imbalanced learning
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35. A survey of human judgement and quantitative forecasting methods
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36. Deep learning based fusion strategies for personality prediction
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37. Modulating the Impacts of Stochastic Uncertainties Linked to Deposit Locations in Data-Driven Predictive Mapping of Mineral Prospectivity
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38. Dynamic adaptive ensemble case-based reasoning: application to stock market prediction
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39. A SEARCH FOR POINT SOURCES OF EeV NEUTRONS
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40. Random forests with parametric entropy-based information gains for classification and regression problems
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41. Recursive Aggregation of Estimators by the Mirror Descent Algorithm with Averaging
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42. A selective deep stacked denoising autoencoders ensemble with negative correlation learning for gearbox fault diagnosis
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43. Deeply uncertain pathways: Integrated multi-city regional water supply infrastructure investment and portfolio management
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44. DBFS: An effective Density Based Feature Selection scheme for small sample size and high dimensional imbalanced data sets
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45. An ensemble based data fusion approach for early diagnosis of Alzheimer’s disease
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46. MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review
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47. Residual Networks are Exponential Ensembles of Relatively Shallow Networks.
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48. On the consistency of supervised learning with missing values
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49. Robust remote sensing retrieval of key eutrophication indicators in coastal waters based on explainable machine learning
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50. Trees Weighting Random Forest Method for Classifying High-Dimensional Noisy Data
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51. Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework
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52. Autostacker
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53. Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directions
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54. A comparison of AdaBoost algorithms for time series forecast combination
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55. Automatic Prediction Of Hit Songs.
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56. POS-RS: A Random Subspace method for sentiment classification based on part-of-speech analysis
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57. Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
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58. Bayesian Neural Networks for Selection of Drug Sensitive Genes
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59. Learning Boolean formulas
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60. Arbitrating Among Competing Classifiers Using Learned Referees
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61. On Weak Learning
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62. Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
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63. Improved SVR machine learning models for agricultural drought prediction at downstream of Langat River Basin, Malaysia
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64. Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures
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65. Generalized monotonic regression based on B-splines with an application to air pollution data
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66. Decision tree-based learning to predict patient controlled analgesia consumption and readjustment
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67. Tree Decomposition for Large-Scale SVM Problems
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68. Research and development of neural network ensembles: a survey
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69. The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning
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70. Automatically Annotating Images with Keywords: A Review of Image Annotation Systems
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71. Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection
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72. An empirical analysis of dropout in piecewise linear networks
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73. Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques
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74. PM2.5 concentration estimation using convolutional neural network and gradient boosting machine
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75. Aggregation models in ensemble learning: A large-scale comparison
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76. Evolving a Behavioral Repertoire for a Walking Robot
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77. A decision support system to facilitate management of patients with acute gastrointestinal bleeding
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78. Modeling oil-brine interfacial tension at high pressure and high salinity conditions
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80. Diagnosing Breast Masses in Digital Mammography Using Feature Selection and Ensemble Methods
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83. Study of corporate credit risk prediction based on integrating boosting and random subspace
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85. Evolving rule-based systems in two medical domains using genetic programming
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86. Improving accuracy for cancer classification with a new algorithm for genes selection
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87. Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition
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88. Enhanced Biologically Inspired Model for Object Recognition
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89. Gradient and Newton boosting for classification and regression
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90. Defect cluster recognition system for fabricated semiconductor wafers
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91. Eye detection for a real-time vehicle driver fatigue monitoring system
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92. Applying Ant Colony Optimization to configuring stacking ensembles for data mining
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93. Multicategory Crowdsourcing Accounting for Variable Task Difficulty, Worker Skill, and Worker Intention
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95. FilterBoost: Regression and Classification on Large Datasets
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96. Comparison of predictive modeling approaches for 30-day all-cause non-elective readmission risk
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97. Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China
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98. A systematic and critical review on development of machine learning based-ensemble models for prediction of adsorption process efficiency
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99. Ensemble Estimators for Multivariate Entropy Estimation
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100. Assessing fracture risk using gradient boosting machine (GBM) models
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