The Strength of Weak Learnability

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Items 1,101–1,200 of 1,455

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3. A Targeted Search for Point Sources of EeV Photons with the Pierre Auger Observatory
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5. Prediction of the distillation temperatures of crude oils using 1H NMR and support vector regression with estimated confidence intervals
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6. ON DESIGNING FUZZY RULE-BASED MULTICLASSIFICATION SYSTEMS BY COMBINING FURIA WITH BAGGING AND FEATURE SELECTION
   Krzysztof Trawiński, Óscar Cordón, Arnaud Quirin · 2011 · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems
7. Weighted K-means support vector machine for cancer prediction
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8. Scaling Kernel-Based Systems to Large Data Sets
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9. Classic Learning
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10. Autostacker: A Compositional Evolutionary Learning System
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11. Machine-Learning Algorithms for Process Condition Data-Based Inclusion Prediction in Continuous-Casting Process: A Case Study
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12. Hybrid modeling design patterns
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13. A model for skin cancer using combination of ensemble learning and deep learning
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14. Anomaly-based error and intrusion detection in tabular data: No DNN outperforms tree-based classifiers
    Tommaso Zoppi, Stefano Gazzini, Andrea Ceccarelli · 2024 · Future Generation Computer Systems
15. Weighted-Object Ensemble Clustering
    Yazhou Ren, Carlotta Domeniconi, Guoji Zhang, Guoxian Yu · 2013 · Proceedings
16. Automatic evidence quality prediction to support evidence-based decision making
    Abeed Sarker, Diego Mollá, Cécile L. Paris · 2015 · Artificial Intelligence in Medicine
17. Univariate and Multivariate Time Series Manifold Learning
    Colin O’Reilly, Klaus Moessner, Michele Nati · 2017 · Knowledge-Based Systems
18. Adaptive Classifier Ensemble Method Based on Spatial Perception for High-Dimensional Data Classification
    Yuhong Xu, Zhiwen Yu, Wenming Cao, C. L. Philip Chen, Jane You · 2019 · IEEE Transactions on Knowledge and Data Engineering
19. Uses and opportunities for machine learning in hypertension research
    Dhammika Amaratunga, Javier Cabrera, Davit Sargsyan, John B. Kostis, Stavros Zinonos, William J. Kostis · 2020 · International Journal of Cardiology Hypertension
20. Predicting Tree Sap Flux and Stomatal Conductance from Drone-Recorded Surface Temperatures in a Mixed Agroforestry System—A Machine Learning Approach
    Florian Ellsäßer, Alexander Röll, Joyson Ahongshangbam, Pierre‐André Waite, Hendrayanto Hendrayanto, Bernhard Schuldt, Dirk Hölscher · 2020 · Remote Sensing
21. Behavior Classification and Analysis of Grazing Sheep on Pasture with Different Sward Surface Heights Using Machine Learning
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22. Machine learning techniques to predict the fundamental period of infilled reinforced concrete frame buildings
    Asma Yahiaoui, Saida Dorbani, Lilya Yahiaoui · 2023 · Structures
23. Fault diagnosis in assembly processes based on engineering-driven rules and PSOSAEN algorithm
    Shichang Du, Lifeng Xi · 2010 · Computers & Industrial Engineering
24. Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System
    Pablo A. D. Castro, Fernando José Von Zuben · 2010 · IEEE Transactions on Neural Networks
25. Generalized Linear Models
    Mario V. Wüthrich, Michael Merz · 2022 · Springer Actuarial
26. Boosted ARTMAP
    Stephen Verzi, Gregory L. Heileman, Michael Georgiopoulos, M. J. R. Healy · 2002 · International Joint Conference on Neural Networks (IJCNN)
27. Ensemble Feature Selection for Rankings of Features
    Borja Seijo-Pardo, Verónica Bolón‐Canedo, Iago Porto-Díaz, Amparo Alonso‐Betanzos · 2015 · Lecture notes in computer science
28. A wavelet-based technique for damage quantification via mode shape decomposition
    Mohammadreza Vafaei, Sophia C. Alih, Ahmad Baharuddin Abd. Rahman, Azlan Adnan · 2014 · Structure and Infrastructure Engineering
29. Intelligent Optimization Methods for High-Dimensional Data Classification for Support Vector Machines
    Sheng Ding, Li Chen · 2010 · Intelligent Information Management
30. Machine learning techniques for web intrusion detection — A comparison
    Truong Son Pham, Tuan-Hao Hoang, Vu Van Canh · 2016 · International Conference on Knowledge and Systems Engineering/Proceedings
31. Prediction of disease based on prescription using data mining methods
    Shiva Kazempour Dehkordi, Hedieh Sajedi · 2018 · Health and Technology
32. Cortical Surface Thickness as a Classifier: Boosting for Autism Classification
    Vikas Singh, Lopamudra Mukherjee, Moo Kwon Chung · 2008 · Lecture notes in computer science
33. Distributed Classifier Based on Genetically Engineered Bacterial Cell Cultures
    Andriy Didovyk, Канаков Олег Игоревич, Mikhail Ivanchenko, Jeff Hasty, Ramón Huerta, Lev S. Tsimring · 2014 · ACS Synthetic Biology
34. Using predictive analysis to improve invoice-to-cash collection
    Sai Zeng, Prem Melville, Christian A. Lang, Ioana M. Boier-Martin, Conrad Murphy · 2008 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
35. Scaling Up a Boosting-Based Learner via Adaptive Sampling
    Carlos Domingo, Osamu Watanabe · 2000 · Lecture notes in computer science
36. Decision tree grafting
    Geoffrey I. Webb · 1997
37. Predicting Is Not Explaining: Targeted Learning of the Dative Alternation
    Antoine Chambaz, Guillaume Desagulier · 2015 · Journal of Causal Inference
38. Sample-screening MKL method via boosting strategy for hyperspectral image classification
    Yanfeng Gu, Huan Liu · 2015 · Neurocomputing
39. Experimental evaluation of ensemble classifiers for imbalance in Big Data
    Mario Juez-Gil, Álvar Arnaiz González, Juan J. Rodríguez, César García‐Osorio · 2021 · Applied Soft Computing
40. Prepare for trouble and make it double! Supervised – Unsupervised stacking for anomaly-based intrusion detection
    Tommaso Zoppi, Andrea Ceccarelli · 2021 · Journal of Network and Computer Applications
41. Sequential optimization three-way decision model with information gain for credit default risk evaluation
    Feng Shen, Xin Zhang, Run Wang, Dao Lan, Wei Hua Zhou · 2022 · International Journal of Forecasting
42. A family of online boosting algorithms
    Boris Babenko, Ming–Hsuan Yang, Serge Belongie · 2009 · IEEE International Conference on Computer Vision Workshops
43. Error-correcting output codes based ensemble feature extraction
    Guoqiang Zhong, Cheng‐Lin Liu · 2012 · Pattern Recognition
44. Hardness of Minimizing and Learning DNF Expressions
    Subhash Khot, Rishi Saket · 2008 · Annual Symposium on Foundations of Computer Science
45. Finite sample performance guarantees of fusers for function estimators
    Nageswara S. V. Rao · 2000 · Information Fusion
46. A Balanced Ensemble Learning with Adaptive Error Functions
    Yong Liu · 2008 · Lecture notes in computer science
47. Distributed community detection in web-scale networks
    Michael Ovelgönne · 2013 · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
48. Multiple Kernel Learning on the Limit Order Book
    Tristan Fletcher, Zakria Hussain, John S. Shawe-Taylor · 2010 · UCL Discovery (University College London)
49. Robust Supervised Learning
    Ilias Diakonikolas, Daniel M. Kane · 2023 · Cambridge University Press eBooks
50. Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method
    Tongan Cai · 2018 · Applied and Computational Mathematics
51. Semantic Labeling of Places with Mobile Robots
    Óscar Martínez Mozos · 2010 · Springer tracts in advanced robotics
52. Machine learning and soft computing for ICT security: an overview of current trends
    Francesco Camastra, Angelo Ciaramella, Antonino Staiano · 2011 · Journal of Ambient Intelligence and Humanized Computing
53. A review of ensemble methods for de novo motif discovery in ChIP-Seq data
    Andrei Lihu, tefan Holban · 2015 · Briefings in Bioinformatics
54. Effects of Concentrated Ownership and Owner Management on Small Business Debt Financing
    Zhenyu Wu, Peggy L. Hedges, Shali Zhang · 2007 · Journal of Small Business Management
55. Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury
    James R. Stone, Elisabeth A. Wilde, Brian A. Taylor, David F. Tate, Harvey S. Levin, Erin D. Bigler, Randall S. Scheibel, Mary R. Newsome, Andrew R. Mayer, Tracy J. Abildskov et al. · 2016 · Brain Injury
56. Having no mathematical model may not secure PUFs
    Fatemeh Ganji, Shahin Tajik, Fabian Fäßler, Jean‐Pierre Seifert · 2017 · Journal of Cryptographic Engineering
57. On the complexity of some inductive logic programming problems
    Georg Gottlob, Nicola Leone, Francesco Scarcello · 1999 · New Generation Computing
58. Resource Allocation in Competitive Multiagent Systems
    Yoav Shoham, Kevin Leyton‐Brown · 2003
59. InferLine: ML Inference Pipeline Composition Framework.
    Daniel Crankshaw, Gur-Eyal Sela, Corey Zumar, Xiangxi Mo, Joseph E. Gonzalez, Ion Gabriel Stoica, Alexey Tumanov · 2018 · arXiv (Cornell University)
60. 2-step Gradient Boosting approach to selectivity bias correction in tax audit: an application to the VAT gap in Italy
    Pierfrancesco Alaimo Di Loro, Daria Scacciatelli, Giovanna Tagliaferri · 2022 · Statistical Methods & Applications
61. Bagging or boosting? Empirical evidence from financial statement fraud detection
    Xiaowei Chen, C. Xiaoyu Zhai · 2023 · Accounting and Finance
62. Prospectivity and Uncertainty Analysis of Tungsten Polymetallogenic Mineral Resources in the Nanling Metallogenic Belt, South China: A Comparative Study of AdaBoost, GBDT, and XgBoost Algorithms
    Tongfei Li, Qinglin Xia, Yongpeng Ouyang, Runling Zeng, Qiankun Liu, Taotao Li · 2024 · Natural Resources Research
63. Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction
    Zhenkun Liu, Ying Zhang, Mohammad Zoynul Abedin, Jianzhou Wang, Hufang Yang, Yuyang Gao, Yinghao Chen · 2024 · Journal of Retailing and Consumer Services
64. Many Hands Make Light Work - On Ensemble Learning Techniques for Data Fusion in Remote Sensing
    Andreas Merentitis, Christian Debes · 2015 · IEEE Geoscience and Remote Sensing Magazine
65. Multiple Boosting in the Ant Colony Decision Forest meta-classifier
    Jan T. Kozák, Urszula Boryczka · 2014 · Knowledge-Based Systems
66. Analysis of a probabilistic model of redundancy in unsupervised information extraction
    Doug Downey, Oren Etzioni, Stephen G. Soderland · 2010 · Artificial Intelligence
67. Automatic aortic valve landmark localization in coronary CT angiography using colonial walk
    Walid Abdullah Al, Ho Yub Jung, Il Dong Yun, Yeonggul Jang, Hyung‐Bok Park, Hyuk‐Jae Chang · 2018 · PLoS ONE
68. Integration of deep neural networks and ensemble learning machines for missing well logs estimation
    Han Jian, Lu Chenghui, Zhimin Cao, Mu Haiwei · 2020 · Flow Measurement and Instrumentation
69. Supervised Machine Learning Techniques: An Overview with Applications to Banking
    Linwei Hu, Jie Chen, Joel Vaughan, Soroush Aramideh, Hanyu Yang, Kelly Wang, Agus Sudjianto, Vijayan N. Nair · 2021 · International Statistical Review
70. Semantic Neural Network Ensemble for Automated Dependency Relation Extraction from Bridge Inspection Reports
    Kaijian Liu, Nora El-Gohary · 2021 · Journal of Computing in Civil Engineering
71. A novel webpage layout aesthetic evaluation model for quantifying webpage layout design
    Hongyan Wan, Wanting Ji, Guoqing Wu, Xiaoyun Jia, Xue Zhan, Mengting Yuan, Ruili Wang · 2021 · Information Sciences
72. Boosting
    Peter Bühlmann, Bin Yu · 2009 · Wiley Interdisciplinary Reviews Computational Statistics
73. The use of machine learning techniques for assessing the potential of organizational resilience
    Tomasz Ewertowski, Buse Çisil Güldoğuş, Semih Kuter, Süreyya Akyüz, Gerhard‐Wilhelm Weber, Joanna Sadłowska-Wrzesińska, Elżbieta Racek · 2023 · Central European Journal of Operations Research
74. Prediction of the Splitting Tensile Strength of Manufactured Sand Based High-Performance Concrete Using Explainable Machine Learning
    Rakesh Kumar, Pijush Samui, Baboo Rai · 2024 · Iranian Journal of Science and Technology Transactions of Civil Engineering
75. Hybrid Visual Servoing by Boosting IBVS and PBVS
    A. H. Abdul Hafez, Enric Cervera, C. V. Jawahar · 2008 · International Conference on Information and Communication Technologies: From Theory to Applications (ICTTA)
76. Text Categorization Based on Boosting Association Rules
    Yongwook Yoon, Gary G. Lee · 2008 · IEEE International Conference on Semantic Computing (ICSC)
77. Boosting the performance of connectionist large vocabulary speech recognition
    Gary Cook, T. Robinson · 2002 · International Conference on Spoken Language Processing (ICSLP)
78. On boosting kernel regression
    Marco Di Marzio, Charles C Taylor · 2007 · Journal of Statistical Planning and Inference
79. Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis
    Nakhle, Farid, Harfouche, Antoine L. · 2021 · Patterns
80. A multiclassifier approach for topology-based WiFi indoor localization
    Krzysztof Trawiński, José M. Alonso, Noelia Hernández · 2013 · Soft Computing
81. BOOSTED DECISION TREES, A POWERFUL EVENT CLASSIFIER
    Byron P. Roe, Hai-Jun Yang, Ji Zhu · 2006 · Conference on Statistical Problems in Particle Physics, Astrophysics and Cosmology (PHYSTAT)
82. Novel evolutionary algorithms for supervised classification problems: an experimental study
    Pu Wang, Thomas Weise, Raymond Chiong · 2011 · Evolutionary Intelligence
83. Confidence-based multiclass AdaBoost for physical activity monitoring
    Attila Reiss, Gustaf Hendeby, Didier Stricker · 2013 · International Symposium on Wearable Computers
84. Robust Pallet Detection for Automated Logistics Operations
    Robert Varga, Sergiu Nedevschi · 2016 · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP)
85. A strong hybrid AdaBoost classification algorithm for speaker recognition
    V. Karthikeyan, S. Suja Priyadharsini · 2021 · Sadhana
86. General Greedy De-Bias Learning
    Xinzhe Han, Shuhui Wang, Chi Su, Qingming Huang, Qi Chuan Tian · 2023 · IEEE Transactions on Pattern Analysis and Machine Intelligence
87. Evaluating StackingC and ensemble models for enhanced lithological classification in geological mapping
    Sasan Farhadi, Samuele Tatullo, Mina Boveiri Konari, Peyman Afzal · 2024 · Journal of Geochemical Exploration
88. Deep Learning for Multilabel Classification of Coral Reef Conditions in the Indo‐Pacific Using Underwater Photo Transect Method
    Xinlei Shao, Hongruixuan Chen, Kirsty Magson, Jiaqi Wang, Jian Song, Jundong Chen, Jun Sasaki · 2024 · Aquatic Conservation Marine and Freshwater Ecosystems
89. Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece
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90. Classification Problem in Imbalanced Datasets
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91. A Machine Learning Approach for Rainfall Estimation Integrating Heterogeneous Data Sources
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92. ADDRESSING IMBALANCED INSURANCE DATA THROUGH ZERO-INFLATED POISSON REGRESSION WITH BOOSTING
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93. Model and variable selection using machine learning methods with applications to childhood stunting in Bangladesh
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94. Applied Machine Learning in Operations Management
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95. Study on predicting compressive strength of concrete using supervised machine learning techniques
    B. Vamsi Varma, Elluri Venkata Prasad, Sudhakar Singha · 2023 · Asian Journal of Civil Engineering
96. A Robust AdaBoost.RT Based Ensemble Extreme Learning Machine
    Pengbo Zhang, Zhi-Xin Yang · 2015 · Mathematical Problems in Engineering
97. Dry film method with ytterbium as the internal standard for near infrared spectroscopic plasma glucose assay coupled with boosting support vector regression
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98. Identification of nonlinear systems using generalized kernel models
    Sheng Chen, Xia Hong, Chris J. Harris, Xunxian Wang · 2005 · IEEE Transactions on Control Systems Technology
99. Ensemble Learning with Multiple Classifiers and Polarimetric Features for Polarized SAR Image Classification
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100. Prediction performance of individual and ensemble learners for chronic kidney disease
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