The Strength of Weak Learnability

Cited by (1,455)

Items 301–400 of 1,455

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2. Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost
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3. Support vector machine based multiagent ensemble learning for credit risk evaluation
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4. A new boosting algorithm for improved time-series forecasting with recurrent neural networks
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5. Compressive strength of concrete material using machine learning techniques
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6. Agnostic Learning of Monomials by Halfspaces Is Hard
   Vitaly Feldman, Venkatesan Guruswami, Prasad Raghavendra, Yi Wu · 2009 · Annual Symposium on Foundations of Computer Science
7. Finding Waldo: Learning about Users from their Interactions
   Eli T. Brown, Alvitta Ottley, Helen Zhao, Quan Lin, Richard Souvenir, Alex Endert, Remco Chang · 2014 · IEEE Transactions on Visualization and Computer Graphics
8. A principle component analysis-based random forest with the potential nearest neighbor method for automobile insurance fraud identification
   Yaqi Li, Chun Gang Yan, Wei Liu, Maozhen Li · 2017 · Applied Soft Computing
9. A novel multi-stage hybrid model with enhanced multi-population niche genetic algorithm: An application in credit scoring
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10. Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis
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11. Multi-features extraction based on deep learning for skin lesion classification
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12. Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods: New Empirical Evidence
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13. Strength prediction of concrete-filled steel tubular columns using Categorical Gradient Boosting algorithm
    Seunghye Lee, Thuc Phuong Vo, Huu‐Tai Thai, Jaehong Lee, Vipulkumar Ishvarbhai Patel · 2021 · Engineering Structures
14. An overtraining-resistant stochastic modeling method for pattern recognition
    Eugene M. Kleinberg · 1996 · The Annals of Statistics
15. Machine learning for the educational sciences
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16. Salient coding for image classification
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17. Computational creativity theory: The FACE and IDEA descriptive models
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18. A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning
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19. Predicting Breast Cancer Recurrence Using Machine Learning Techniques
    Pedro Henriques Abreu, Miriam Seoane Santos, Miguel Henriques Abreu, Bruno Andrade, Daniel Castro Silva · 2016 · ACM Computing Surveys
20. A data augmentation approach to XGboost-based mineral potential mapping: An example of carbonate-hosted Zn Pb mineral systems of Western Iran
    Mohammad Parsa · 2021 · Journal of Geochemical Exploration
21. Equivalence of models for polynomial learnability
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22. On the difficulty of approximately maximizing agreements
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23. Semi-Supervised Learning under Class Distribution Mismatch
    Yanbei Chen, Xiatian Zhu, Wei Li, Shaogang Gong · 2020 · Proceedings of the AAAI Conference on Artificial Intelligence
24. Machine learning and interactive GUI for concrete compressive strength prediction
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25. Comparison of histogram-based gradient boosting classification machine, random Forest, and deep convolutional neural network for pavement raveling severity classification
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26. An Ensemble Pruning Primer
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27. Comparison of Bagging, Boosting and Stacking Ensembles Applied to Real Estate Appraisal
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28. Adaboost Algorithm in Artificial Intelligence for Optimizing the IRI Prediction Accuracy of Asphalt Concrete Pavement
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29. The online performance estimation framework: heterogeneous ensemble learning for data streams
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30. Application of a new machine learning model to improve earthquake ground motion predictions
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31. Boosting textual compression in optimal linear time
    Paolo Ferragina, Raffaele Giancarlo, Giovanni Manzini, Marinella Sciortino · 2005 · Journal of the ACM
32. Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America
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33. Machine-learning methods for stream water temperature prediction
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34. Stochastic configuration networks ensemble with heterogeneous features for large-scale data analytics
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35. A portfolio approach to algorithm select
    Kevin Leyton‐Brown, Eugene Nudelman, Galen Andrew, Jim McFadden, Yoav Shoham · 2003 · International Joint Conference on Artificial Intelligence
36. Ensemble Deep Learning for Diabetic Retinopathy Detection Using Optical Coherence Tomography Angiography
    Morgan Heisler, Sonja G. Karst, Julian Lo, Zaid N. Mammo, Timothy T. Yu, Simon J. Warner, David Maberley, Mirza Faisal Beg, Eduardo Vitor Navajas, Marinko V. Šarunic · 2020 · Translational Vision Science & Technology
37. Combining Classifiers: From the Creation of Ensembles to the Decision Fusion
    Moacir Antonelli Ponti · 2011 · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
38. On Network Design Spaces for Visual Recognition
    Radosavovic, Ilija, Johnson, Justin, Xie, Saining, Lo, Wan-Yen, Dollar, Piotr · 2019 · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
39. Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model
    Rahim Barzegar, Asghar Asghari Moghaddam, Jan Adamowski, Bogdan Ozga-Zieliński · 2017 · Stochastic Environmental Research and Risk Assessment
40. Robust supervised classification with mixture models: Learning from data with uncertain labels
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41. Bootstrap - Inspired Techniques in Computation Intelligence
    Robi Polikar A · 2007 · IEEE Signal Processing Magazine
42. Limiting the Number of Trees in Random Forests
    Patrice Latinne, Olivier Debeir, Christine Decaestecker · 2001 · Lecture notes in computer science
43. A weight-adjusted voting algorithm for ensembles of classifiers
    Hyunjoong Kim, Hyeuk Kim, Hojin Moon, Hongshik Ahn · 2011 · Journal of the Korean Statistical Society
44. Ensemble feature selection in high dimension, low sample size datasets: Parallel and serial combination approaches
    Chih‐Fong Tsai, Ya-Ting Sung · 2020 · Knowledge-Based Systems
45. Dynamic ensemble extreme learning machine based on sample entropy
    Junhai Zhai, Hongyu Xu, Xizhao Wang · 2012 · Soft Computing
46. State-of-the-art Anonymization of Medical Records Using an Iterative Machine Learning Framework
    György Szarvas, Richárd Farkas, Róbert Busa‐Fekete · 2007 · Journal of the American Medical Informatics Association
47. Classification of seismic signals by integrating ensembles of neural networks
    Yair Shimshoni, Nathan Intrator · 1998 · IEEE Transactions on Signal Processing
48. A polynomial-time algorithm for learning noisy linear threshold functions
    Avrim L. Blum, ALAN M. FRIEZE, Ravi Kannan, Santosh Vempala · 2002 · Annual Symposium on Foundations of Computer Science
49. Less is More
    Shebuti Rayana, Leman Akoglu · 2016 · ACM Transactions on Knowledge Discovery from Data
50. Mining Big Data Using Parsimonious Factor, Machine Learning, Variable Selection and Shrinkage Methods
    Hyun Hak Kim, Norman Rasmus Swanson · 2016 · International Journal of Forecasting
51. Boosting ridge regression
    Gerhard Tutz, Harald Binder · 2006 · Computational Statistics & Data Analysis
52. Traffic Flow Prediction Using Adaboost Algorithm With Random Forests As A Weak Learner
    Guy Leshem, Ya'acov Ritov · 2007
53. Tissue classification with gene expression profiles
    Amir Ben‐Dor, Laurakay Bruhn, Nir Friedman, Iftach Nachman, Michèl Schummer, Zohar Yakhini B · 2000 · Research in Computational Molecular Biology
54. Machine learning techniques
    Abdülhamit Subaşı · 2020 · Elsevier eBooks
55. Artificial Intelligence and Advanced Materials
    Cefe López · 2022 · Advanced Materials
56. An Ensemble-Based Incremental Learning Approach to Data Fusion
    Devi Parikh, Robi Polikar A · 2007 · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
57. The boosting: A new idea of building models
    Dongsheng Cao, Qing‐Song Xu, Yi‐Zeng Liang, Liangxiao Zhang, Hong‐Dong Li · 2009 · Chemometrics and Intelligent Laboratory Systems
58. Understanding Complex Datasets: Data Mining with Matrix Decompositions
    David B. Skillicorn · 2007
59. Multi-Net Systems
    AMANDA J. C. SHARKEY · 1999 · Perspectives in neural computing
60. Disease diagnostics using machine learning of B cell and T cell receptor sequences
    Maxim Zaslavsky, Erin Craig, Jackson Michuda, Nidhi Sehgal, Nikhil Ram-Mohan, Ji-Yeun Lee, Khoa D. Nguyen, Ramona A. Hoh, Tho D. Pham, Katharina Röltgen et al. · 2025 · Science
61. An adaptive version of the boost by majority algorithm
    Yoav Freund · 1999 · Conference on Learning Theory
62. On the boosting ability of top-down decision tree learning algorithms
    Michael J. Kearns, Yishay Mansour · 1996 · ACM Symposium on Theory of Computing (STOC)
63. Empirical hardness models
    Kevin Leyton‐Brown, Eugene Nudelman, Yoav Shoham · 2009 · Journal of the ACM
64. Improved Word-Level System Combination for Machine Translation
    Antti-Veikko I. Rosti, Spyros Matsoukas, Richard M. Schwartz · 2007
65. Performance evaluation of automated approaches to building detection in multi-source aerial data
    Kourosh Khoshelham, Carla Nardinocchi, Emanuele Frontoni, Adriano Mancini, Primo Zingaretti · 2009 · ISPRS Journal of Photogrammetry and Remote Sensing
66. A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data
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67. Quickly Boosting Decision Trees - Pruning Underachieving Features Early
    Ron D. Appel, Thomas J. Fuchs, Piotr Dollár, Pietro Perona · 2013 · CaltechAUTHORS (California Institute of Technology)
68. Short-term rockburst risk prediction using ensemble learning methods
    Weizhang Liang, Asli Sari, Guoyan Zhao, Stephen D. McKinnon, Hao Wu · 2020 · Natural Hazards
69. Boosting as entropy projection
    Jyrki Kivinen, Manfred K. Warmuth · 1999 · Conference on Learning Theory
70. Random Projection, Margins, Kernels, and Feature-Selection
    Avrim L. Blum · 2006 · Lecture notes in computer science
71. Distributed Learning, Communication Complexity and Privacy
    Maria-Florina Balcan, Avrim L. Blum, Shai Fine, Yishay Mansour · 2012 · arXiv (Cornell University)
72. A Modern Take on the Bias-Variance Tradeoff in Neural Networks
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73. Theoretical Views of Boosting
    Robert E. Schapire · 1999 · Lecture notes in computer science
74. Feature Selection with Annealing for Computer Vision and Big Data Learning
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75. Learning-based scheduling of flexible manufacturing systems using ensemble methods
    Paolo Priore, Borja Ponte, Javier Puente, Alberto Gómez Gómez · 2018 · Computers & Industrial Engineering
76. BOOSTING: WHY YOU CAN USE THE HP FILTER
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77. Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran
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78. Predicting the Outcome of Construction Litigation Using Boosted Decision Trees
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79. A Gradient-Based Boosting Algorithm for Regression Problems
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80. A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation
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81. Bankruptcy Prediction Using Machine Learning Techniques
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82. Combining the predictions of multiple classifiers: using competitive learning to initialize neural networks
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83. Data mining tasks and methods: Classification: decision-tree discovery
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84. A Principal Components Approach to Combining Regression Estimates
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85. Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal
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86. How does urban heat island differ across urban functional zones? Insights from 2D/3D urban morphology using geospatial big data
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87. Foundations of Machine Learning
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88. Human-in-the-Loop Person Re-identification
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89. A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies
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90. Data mining for improving a cleaning process in the semiconductor industry
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91. Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
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92. Biologically Inspired Modular Neural Networks
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93. Data‐driven spatial b value estimation with applications to California seismicity: To b or not to b
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94. Improving financial bankruptcy prediction in a highly imbalanced class distribution using oversampling and ensemble learning: a case from the Spanish market
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95. A Multilingual Named Entity Recognition System Using Boosting and C4.5 Decision Tree Learning Algorithms
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96. Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery
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97. Ensemble Methods
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98. Unsupervised Algorithms to Detect Zero-Day Attacks: Strategy and Application
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99. User preferences based software defect detection algorithms selection using MCDM
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100. An ensemble uncertainty aware measure for directed hill climbing ensemble pruning
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