The Strength of Weak Learnability

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Items 401–500 of 1,455

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10. Time series forecasting using ensemble learning methods for emergency prevention in hydroelectric power plants with dam
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12. AdaBoost$^{+}$: An Ensemble Learning Approach for Estimating Weather-Related Outages in Distribution Systems
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13. ada: AnRPackage for Stochastic Boosting
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14. Special Invited Paper-Additive logistic regression: A statistical view of boosting
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17. Neural Networks and Deep Learning
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18. Greedy Gradient Ensemble for Robust Visual Question Answering
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19. A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights
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20. Boosted Mixture of Experts: An Ensemble Learning Scheme
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21. Cancer classification using ensemble of neural networks with multiple significant gene subsets
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22. Statistical Learning Methods Applied to Process Monitoring: An Overview and Perspective
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23. On developing an automatic threshold applied to feature selection ensembles
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24. Machine learning assisted prediction of the Young’s modulus of compositionally complex alloys
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25. On the Boosting Ability of Top–Down Decision Tree Learning Algorithms
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26. Estimation of ice thickness on lakes using artificial neural network ensembles
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27. Classification by ensembles from random partitions of high-dimensional data
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28. A large empirical assessment of the role of data balancing in machine-learning-based code smell detection
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29. Ensemble of optimal trees, random forest and random projection ensemble classification
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30. Application of Gradient Boosting Machine Learning Algorithms to Predict Uniaxial Compressive Strength of Soft Sedimentary Rocks at Thar Coalfield
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31. Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory
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32. Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization
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33. An experimental study of the learnability of congestion control
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34. An Introduction to Feature Selection
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35. The power of ensemble learning in sentiment analysis
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36. Locality-constrained and spatially regularized coding for scene categorization
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37. A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes
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38. Multiple classifiers in biometrics. part 1: Fundamentals and review
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39. Ensemble One-Dimensional Convolution Neural Networks for Skeleton-Based Action Recognition
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40. Prediction of seven-day compressive strength of field concrete
    Xueqing Zhang, Muhammad Zeshan Akber, Wei Zheng · 2021 · Construction and Building Materials
41. Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis
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42. MizAR 40 for Mizar 40
    Cezary Kaliszyk, Josef Urban · 2015 · Journal of Automated Reasoning
43. Combining Bagging and Random Subspaces to Create Better Ensembles
    Panče Panov, Sašo Džeroski · 2007 · Lecture notes in computer science
44. Boosting Regression Estimators
    Ran Avnimelech, Nathan Intrator · 1999 · Neural Computation
45. Twenty Questions with Noise: Bayes Optimal Policies for Entropy Loss
    Bruno Jedynak, Peter I. Frazier, Raphael Sznitman · 2012 · Journal of Applied Probability
46. Bagging-based spectral clustering ensemble selection
    Jian‐Hua Jia, Xuan Xiao, Bingxiang Liu, Licheng Jiao · 2011 · Pattern Recognition Letters
47. Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China
    Xudong Hu, Han Zhang, Hongbo Mei, Dunhui Xiao, Yuanyuan Li, Mengdi Li · 2020 · Applied Sciences
48. Ensemble methods for classification of patients for personalized medicine with high-dimensional data
    Hojin Moon, Hongshik Ahn, Ralph L. Kodell, Songjoon Baek, Chien-Ju Lin, James J. Chen · 2007 · Artificial Intelligence in Medicine
49. EROS: Ensemble rough subspaces
    Qinghua Hu, Daren Yu, Zongxia Xie, Xiaodong Li · 2007 · Pattern Recognition
50. Global sensitivity analysis for a numerical model of radionuclide migration from the RRC “Kurchatov Institute” radwaste disposal site
    Elena Volkova, Bertrand Iooss, François Van Dorpe · 2006 · Stochastic Environmental Research and Risk Assessment
51. AdaBoost is Consistent
    Peter L. Bartlett, Mikhail Traskin · 2007 · The MIT Press eBooks
52. Cluster-Oriented Ensemble Classifier: Impact of Multicluster Characterization on Ensemble Classifier Learning
    Brijesh Verma, Ashfaqur Rahman · 2011 · IEEE Transactions on Knowledge and Data Engineering
53. Concept Drift Detection and Adaption in Big Imbalance Industrial IoT Data Using an Ensemble Learning Method of Offline Classifiers
    Chun‐Cheng Lin, Der‐Jiunn Deng, Chin-Hung Kuo, Linnan Chen · 2019 · IEEE Access
54. Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning
    Yu Lu, Xianghua Fu, Fangxiong Chen, Kelvin K. L. Wong · 2019 · Artificial Intelligence in Medicine
55. Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market
    Dalil Hadjout, José Francisco Torres, Alicia Troncoso, Abderrazak Sebaa, Francisco Martinez Alvarez · 2021 · Energy
56. A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: from mitigation to exploitation
    Melika Payvand, Manu V Nair, Lorenz K. Müller, Giacomo Indiveri · 2018 · Faraday Discussions
57. Multivariate statistical analysis methods in QSAR
    Somayeh Pirhadi, Fereshteh Shiri, Jahan Bakhsh Ghasemi · 2015 · RSC Advances
58. Pattern Classification and Learning Theory
    Gábor Lugosi · 2002 · Springer eBooks
59. Forecasting stock market for an efficient portfolio by combining XGBoost and Hilbert–Huang transform
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60. Steering in a Random Forest
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61. A Polynomial-Time Algorithm for Learning Noisy Linear Threshold Functions
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62. Evolutionary extreme learning machine ensembles with size control
    Dianhui Wang, Monther Alhamdoosh · 2012 · Neurocomputing
63. On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition
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64. Modeling interfacial tension of the hydrogen-brine system using robust machine learning techniques: Implication for underground hydrogen storage
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65. Synthesis Lectures on Data Management
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66. Stagewise Lasso
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67. Are SonarQube Rules Inducing Bugs?
    Valentina Lenarduzzi, Francesco Lomio, Heikki J. Huttunen, Davide Taibi · 2020 · Tampere University Institutional Repository (Tampere University)
68. Toward robust early-warning models: a horse race, ensembles and model uncertainty
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69. Regional low‐flow frequency analysis using single and ensemble artificial neural networks
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70. Double random forest
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71. Classification in the presence of class noise using a probabilistic Kernel Fisher method
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72. Trade-Off Between Accuracy and Interpretability for PredictiveIn SilicoModeling
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73. Selecting Representative Data Sets
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74. Learning DNF over the Uniform Distribution Using a Quantum Example Oracle
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75. Bidirectional Active Learning: A Two-Way Exploration Into Unlabeled and Labeled Data Set
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76. Boosting algorithms in energy research: a systematic review
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77. An Efficient Approach for Phishing Detection using Machine Learning
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78. Data classification using an ensemble of filters
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79. Learn++.MF: A random subspace approach for the missing feature problem
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80. ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images
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81. A comparative study of Artificial Intelligence methods for project duration forecasting
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82. Novel Layered Clustering-Based Approach for Generating Ensemble of Classifiers
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83. RBoost: Label Noise-Robust Boosting Algorithm Based on a Nonconvex Loss Function and the Numerically Stable Base Learners
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84. Ensemble Learning Methods for Anomaly Intrusion Detection System in Smart Grid
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85. Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications
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86. Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction
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87. Slope Stability Analysis Using Rf, Gbm, Cart, Bt and Xgboost
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88. Boosting proportional hazards models using smoothing splines, with applications to high-dimensional microarray data
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89. Artificial Intelligence for Electricity Supply Chain automation
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90. MARK-ELM: Application of a novel Multiple Kernel Learning framework for improving the robustness of Network Intrusion Detection
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91. Webis: An Ensemble for Twitter Sentiment Detection
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92. Ensemble Learning for Named Entity Recognition
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93. Ensemble Learning
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95. Learned cardinality estimation
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96. Bagging and Boosting Negatively Correlated Neural Networks
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97. Extreme Gradient Boosting Based Tuning for Classification in Intrusion Detection Systems
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98. Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm
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99. Multi-Stream Deep Neural Network for Diabetic Retinopathy Severity Classification Under a Boosting Framework
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100. Genetic programming in classifying large-scale data: an ensemble method
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