The Strength of Weak Learnability

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Items 101–200 of 1,455

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13. Boosting and Other Ensemble Methods
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14. Lung cancer cell identification based on artificial neural network ensembles
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15. SVM and SVM Ensembles in Breast Cancer Prediction
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18. Ensemble of online sequential extreme learning machine
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19. Actively Searching for an Effective Neural Network Ensemble
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21. Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models
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22. Gradient modeling of conifer species using random forests
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23. AdaBoost.RT: a boosting algorithm for regression problems
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24. Classifier ensembles for land cover mapping using multitemporal SAR imagery
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25. Pasting Small Votes for Classification in Large Databases and On-Line
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26. Model averaging prediction by K-fold cross-validation
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27. Maximizing land cover classification accuracies produced by decision trees at continental to global scales
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28. Credit risk assessment with a multistage neural network ensemble learning approach
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29. Boosting Algorithms: A Review of Methods, Theory, and Applications
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30. Foundations of Data Science
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31. Quantum Machine Learning: What Quantum Computing Means to Data Mining
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32. Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)
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33. Ensemble feature selection: Homogeneous and heterogeneous approaches
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34. Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
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35. Residual networks behave like ensembles of relatively shallow networks
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36. Machine Learning in Materials Science
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37. Algorithm Selection for Combinatorial Search Problems: A Survey
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38. Online Learning: Theory, Algorithms, and Applications
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39. Aggregation for Gaussian regression
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40. Boosting Neural Networks
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41. The evaluation strip: A new and robust method for plotting predicted responses from species distribution models
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42. Popular ensemble methods
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43. A gradient boosting approach to the Kaggle load forecasting competition
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44. Machine learning in geo- and environmental sciences: From small to large scale
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45. Ensembles of Learning Machines
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46. An Adaptive Version of the Boost by Majority Algorithm
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47. Are We Preparing for a Good AI Society? A Bibliometric Review and Research Agenda
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48. Predicting short-term stock prices using ensemble methods and online data sources
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49. Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR
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50. Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability
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51. On the learnability of discrete distributions
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52. Combustion machine learning: Principles, progress and prospects
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53. On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data
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54. Recommendations as Treatments: Debiasing Learning and Evaluation
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55. Cluster and Classification Techniques for the Biosciences
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56. Ensemble with neural networks for bankruptcy prediction
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57. Using output codes to boost multiclass learning problems
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58. PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
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59. Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension
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60. Earthquake magnitude prediction in Hindukush region using machine learning techniques
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61. A Survey on Text Classification Algorithms: From Text to Predictions
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62. Computer age statistical inference algorithms, evidence, and data science
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63. A survey of intrusion detection systems based on ensemble and hybrid classifiers
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64. A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection
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65. Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography
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66. Probabilistic Incremental Program Evolution
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67. Semiparametric regression during 2003–2007
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68. A novel ensemble method for credit scoring: Adaption of different imbalance ratios
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69. Defending Yarbus: Eye movements reveal observers' task
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70. Constructing boosting algorithms from SVMs: an application to one-class classification
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71. A data-driven statistical model for predicting the critical temperature of a superconductor
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72. Bagged support vector machines for emotion recognition from speech
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73. Gradient boosting trees for auto insurance loss cost modeling and prediction
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74. Error Reduction through Learning Multiple Descriptions
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75. Active Learning with Multiple Views
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76. Experiments with Classifier Combining Rules
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77. Current Trends and Applications of Machine Learning in Tribology—A Review
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78. A Voting Ensemble Classifier for Wafer Map Defect Patterns Identification in Semiconductor Manufacturing
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79. Artificial Intelligence and Parametric Construction Cost Estimate Modeling: State-of-the-Art Review
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80. Boosting Decision Trees
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81. Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression
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82. Multiple Kernel Learning for Hyperspectral Image Classification: A Review
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83. Model-Based Clustering and Classification for Data Science
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84. Extracting comprehensible models from trained neural networks
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85. Semi-Supervised and Unsupervised Deep Visual Learning: A Survey
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86. Cooperative Coevolution of Artificial Neural Network Ensembles for Pattern Classification
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87. Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset
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88. Inductive inference, DFAs, and computational complexity
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89. Ensemble Learning
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90. Biomarkers for the Diagnosis and Prognosis of Mild Traumatic Brain Injury/Concussion
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91. Review of Classifier Combination Methods
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92. Supervised Learning of Places from Range Data using AdaBoost
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93. An Efficient Membership-Query Algorithm for Learning DNF with Respect to the Uniform Distribution
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94. A literature survey of active machine learning in the context of natural language processing
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95. DATA STREAM MINING A Practical Approach
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96. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction
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97. Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost
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98. Multiple classifier architectures and their application to credit risk assessment
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99. Using Correspondence Analysis to Combine Classifiers
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100. Generalized Additive Modeling with Implicit Variable Selection by Likelihood‐Based Boosting
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