The Strength of Weak Learnability

Cited by (1,455)

Items 701–800 of 1,455

1. Compressive Sequential Learning for Action Similarity Labeling
   Qin, Jie, Liu, Li, Zhang, Zhaoxiang, Wang, Yunhong, Shao, Ling · 2016 · IEEE Transactions on Image Processing
2. Research on learning behavior patterns from the perspective of educational data mining: Evaluation, prediction and visualization
   Guiyun Feng, Muwei Fan · 2023 · Expert Systems with Applications
3. Cluster Based Ensemble Classification for Intrusion Detection System
   M. A. Jabbar, Rajanikanth Aluvalu, S. Sai Satyanarayana Reddy · 2017 · International Conference on Machine Learning and Computing (ICMLC)
4. Reservoir Properties from Well Logs using neural Networks
   Alpana Bhatt · 2002
5. Bagging and Boosting with Dynamic Integration of Classifiers
   Alexey Tsymbal, Seppo J. Puuronen · 2000 · Lecture notes in computer science
6. Boosting Schema Matchers
   Anan Marie, Avigdor Gal · 2008 · Lecture notes in computer science
7. On the necessity of Occam algorithms
   R. Board, Leonard Pitt · 1990 · ACM Symposium on Theory of Computing (STOC)
8. Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach
   Jiang Wang, Stefan Chmiela, Klaus‐Robert Müller, Frank Noé, Cecilia Clementi · 2020 · The Journal of Chemical Physics
9. Landslide Susceptibility Mapping and Driving Mechanisms in a Vulnerable Region Based on Multiple Machine Learning Models
   Haiwei Yu, Wenjie Pei, Jingyi Zhang, Guangsheng Chen · 2023 · Remote Sensing
10. A local boosting algorithm for solving classification problems
    Chunxia Zhang, Jiangshe Zhang · 2007 · Computational Statistics & Data Analysis
11. Feature combination using boosting
    Xu-Cheng Yin, Changping Liu, Zhi Jun Han · 2005 · Pattern Recognition Letters
12. A Hierarchical Ensemble of ECOC for cancer classification based on multi-class microarray data
    Kunhong Liu, Zhi-Hao Zeng, Vincent Ng · 2016 · Information Sciences
13. Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm
    Wenqing Xu, Like Ning, Yong Ming Luo · 2020 · Atmosphere
14. Empirical Asset Pricing via Machine Learning
    Shihao Gu, Bryan T. Kelly, Dacheng Xiu · 2018 · SSRN Electronic Journal
15. Multi-agent adaptive boosting on semi-supervised water supply clusters
    Manuel Herrera, Joaquín Izquierdo, Rafael Pérez-García, Idel Montalvo · 2012 · Advances in Engineering Software
16. Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams
    Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren · 2015 · Proceedings
17. Gender Classification of Walkers via Underfloor Accelerometer Measurements
    Dustin Bales, Pablo A. Tarazaga, Mary E. F. Kasarda, Dhruv Batra, Americo G. Woolard, Jeffrey D. Poston, Vijaya V. N. Sriram Malladi · 2016 · IEEE Internet of Things Journal
18. Improving hazard characterization in microbial risk assessment using next generation sequencing data and machine learning: Predicting clinical outcomes in shigatoxigenic Escherichia coli
    Patrick Murigu Kamau Njage, Pimlapas Leekitcharoenphon, Tine Hald · 2018 · International Journal of Food Microbiology
19. Artificial intelligence methods for selection of an optimized sensor array for identification of volatile organic compounds
    Robi Polikar A, Ruth Shinar, Лалита Удпа, Marc D. Porter · 2001 · Sensors and Actuators B Chemical
20. Bio-Inspired Credit Risk Analysis
    Lean Yu, Shouyang Wang, Kin Keung Lai, Ligang Zhou · 2008 · Springer eBooks
21. An experimental study of the learnability of congestion control
    Anirudh Sivaraman, Keith Winstein, Pratiksha Thaker, Hari Balakrishnan · 2014 · ACM SIGCOMM Computer Communication Review
22. PENALIZED LINEAR UNBIASED SELECTION
    Cun‐Hui Zhang · 2007
23. Robust inference with simple cognitive models
    Henry Brighton · 2006 · Max Planck Institute for Plasma Physics
24. A Near-Term Quantum Computing Approach for Hard Computational Problems in Space Exploration
    Vadim Smelyanskiy, Eleanor Gilbert Rieffel, Sergey Knysh, Colin P. Williams, Mark W. Johnson, M. C. Thom, William G. Macready, Kristen L. Pudenz · 2012 · arXiv (Cornell University)
25. Deep Ensemble Machine for Video Classification
    Jiewan Zheng, Xianbin Cao, Baochang Zhang, Xiantong Zhen, Xiangbo Su · 2018 · IEEE Transactions on Neural Networks and Learning Systems
26. How to improve machine learning models for lithofacies identification by practical and novel ensemble strategy and principles
    Shaoqun Dong, Yan-Ming Sun, Tao Xu, Lianbo Zeng, Xiangyi Du, Xu Yang, Yu Liang · 2022 · Petroleum Science
27. A survey of deep learning applications in cryptocurrency
    Junhuan Zhang, Kewei Cai, Jiaqi Wen · 2023 · iScience
28. A New Constructive Algorithm for Architectural and Functional Adaptation of Artificial Neural Networks
    Md. Monirul Islam, Abdus Sattar, Md. Faijul Amin, Xin Yao, Kazuyuki Murase · 2009 · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
29. New margin-based subsampling iterative technique in modified random forests for classification
    Wei Feng, Gabriel Dauphin, Wenjiang Huang, Yinghui Quan, Wenzhi Liao · 2019 · Knowledge-Based Systems
30. An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
    Jeffrey C. Jackson · 2002 · Annual Symposium on Foundations of Computer Science
31. On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines
    Shichang Du, Jun Lv, Lifeng Xi · 2012 · International Journal of Production Research
32. Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review
    Katya L Masconi, Tandi Edith Matsha, Justin Basile Echouffo-Tcheugui, Rajiv Timothy Erasmus, André Pascal Kengne · 2015 · The EPMA Journal
33. Prediction of water saturation in a tight gas sandstone reservoir by using four intelligent methods: a comparative study
    Sadegh Baziar, Habibollah Bavarsad Shahripour, Mehdi Tadayoni, Majid Nabi‐Bidhendi · 2016 · Neural Computing and Applications
34. Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence
    Haytham H. Elmousalami, Mahmoud Elaskary · 2020 · Journal of Petroleum Exploration and Production Technology
35. Fast Object Detection with Occlusions
    Yen-Yu Lin, Tyng-Luh Liu, C. Fuh · 2004 · European Conference on Computer Vision
36. A Comparison of Ensemble Creation Techniques
    Robert E. Banfield, Lawrence Hall, Kevin W. Bowyer, Divya Bhadoria, W. Philip Kegelmeyer, Steven A. Eschrich · 2004 · Lecture notes in computer science
37. Artificial Neural Network Learning: A Comparative Review
    Costas K. Neocleous, Christos N. Schizas · 2002 · Lecture notes in computer science
38. Data-driven modeling to predict adsorption of hydrogen on shale kerogen: Implication for underground hydrogen storage
    Shams Kalam, Muhammad Arif, Arshad Raza, Najeebullah Lashari, Mohamed Mahmoud · 2023 · International Journal of Coal Geology
39. Automatic seizure detection using Stockwell transform and boosting algorithm for long-term EEG
    Aiyu Yan, Weidong Zhou, Qi Yuan, Shasha Yuan, Qi Wu, Xiuhe Zhao, Jiwen Wang · 2015 · Epilepsy & Behavior
40. Multi-scale Conditional Random Fields for first-person activity recognition
    Kai Zhan, Steven G. Faux, Fabio Tozeto Ramos · 2014 · IEEE International Conference on Pervasive Computing and Communications
41. Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas
    Alessandro Lapini, S. Pettinato, Emanuele Santi, S. Paloscia, Giacomo Fontanelli, Andrea Garzelli · 2020 · Remote Sensing
42. Macroeconomic forecasting using factor models and machine learning: an application to Japan
    Kohei Maehashi, Mototsugu Shintani · 2020 · Journal of the Japanese and International Economies
43. Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting
    Tobias Albrecht, Theresa Maria Rausch, Nicholas Daniel Derra · 2020 · Journal of Business Research
44. Inflammatory bowel disease biomarkers of human gut microbiota selected via different feature selection methods
    Burcu Bakır-Güngör, Hilal Hacılar, Amhar Jabeer, Özkan Ufuk Nalbantoğlu, Oya Aran, Malik Yousef · 2022 · PeerJ
45. Machine learning \& artificial intelligence in the quantum domain
    Vedran Dunjko, Hans J. Briegel · 2017 · arXiv (Cornell University)
46. Case Study: French Motor Third-Party Liability Claims
    Alexander Noll, Robert Salzmann, Mario V. Wüthrich · 2018 · SSRN Electronic Journal
47. Empirical Asset Pricing via Machine Learning
    Gu, Shihao, Kelly, Bryan T., Xiu, Dacheng · 2018 · The Review of Financial Studies
48. Curriculum Learning by Dynamic Instance Hardness
    Tianyi Zhou, Shengjie Wang, Jeffrey A. Bilmes · 2020 · neural information processing systems
49. Some Theory for Generalized Boosting Algorithms
    Peter J. Bickel, Ya’acov Ritov, Alon Zakai · 2006
50. Boosting and Microarray Data
    Philip M. Long, Vinsensius Berlian Vega · 2003 · Machine Learning
51. Data Mining and Visualization
    Ron Kohavi · 2000
52. Neyman-Pearson classification, convexity and stochastic constraints
    Philippe Rigollet, Xin Tong · 2011 · arXiv (Cornell University)
53. Applying a Neural Network Ensemble to Intrusion Detection
    Simone A. Ludwig · 2019 · Journal of Artificial Intelligence and Soft Computing Research
54. Rapid detection of fake news based on machine learning methods
    Barbara Probierz, Piotr Stefański, Jan T. Kozák · 2021 · Procedia Computer Science
55. Sparse support vector regression based on orthogonal forward selection for the generalised kernel model
    X.X. Wang, Sheng Chen, D. Lowe, Chris J. Harris · 2006 · Neurocomputing
56. Improved Bagging Algorithm for Pattern Recognition in UHF Signals of Partial Discharges
    Tianyan Jiang, Jian Li, Yuanbing Zheng, Caixin Sun · 2011 · Energies
57. Comparison among Methods of Ensemble Learning
    Shaohua Wan, Hua Yang · 2013 · International Symposium on Biometrics and Security Technologies
58. Application of boosting to classification problems in chemometrics
    M.H. Zhang, Qingsong Xu, Frits F. D. Daeyaert, Paul J Lewi, Desire Luc Massart · 2005 · Analytica Chimica Acta
59. OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records
    Jonathan H. Chen, Tanya Podchiyska, Russ Biagio Altman · 2015 · Journal of the American Medical Informatics Association
60. A Fully Automated Adjustment of Ensemble Methods in Machine Learning for Modeling Complex Real Estate Systems
    José Luis Alfaro Navarro, Emilio L. Cano, Esteban Alfaro, Noelia García, Matías Gámez, Beatriz Larraz · 2020 · Complexity
61. Boosting in the presence of noise
    Adam Tauman Kalai, Rocco A. Servedio · 2004 · Journal of Computer and System Sciences
62. Learning from data: a tutorial with emphasis on modern pattern recognition methods
    M. Pardo, Giorgio Sberveglieri · 2002 · IEEE Sensors Journal
63. Large Margin Distribution Learning
    Zhi‐Hua Zhou · 2014 · Lecture notes in computer science
64. Statistical queries and faulty PAC oracles
    Scott Evan Decatur · 1993 · Conference on Learning Theory
65. Optimal Trees Selection for Classification via Out-of-Bag Assessment and Sub-Bagging
    Zardad Khan, Naz Gul, Nosheen Faiz, Asma Gul, Werner Adler, Berthold Lausen · 2021 · IEEE Access
66. AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics
    Olivér Hornyák, László Barna Iantovics · 2023 · Mathematics
67. An integrated machine learning and quantitative optimization method for designing sustainable bioethanol supply chain networks
    Mohsen Momenitabar, Zhila Dehdari Ebrahimi, Atefeh Abdollahi, Wesam Helmi, Kelly Bengtson, Peiman Ghasemi · 2023 · Decision Analytics Journal
68. Improving milling force predictions: A hybrid approach integrating physics-based simulation and machine learning for remarkable accuracy across diverse unseen materials and tool types
    Arash Ebrahimi Araghizad, Farzad Pashmforoush, Faraz Tehranizadeh, Kemal Kılıç, Erhan Budak · 2024 · Journal of Manufacturing Processes
69. Advancing groundwater quality predictions: Machine learning challenges and solutions
    Juan Antonio Torres-Martínez, Jürgen Mahlknecht, Manish Kumar, Frank J. Loge, Dugin Kaown · 2024 · The Science of The Total Environment
70. Using Soft Computing to Analyze Inspection Results for Bridge Evaluation and Management
    Zhe Li, Rigoberto Burgueño · 2010 · Journal of Bridge Engineering
71. Optimization by Gradient Boosting
    Gérard Biau A, Benoît Cadre B · 2021 · Springer eBooks
72. Sentiment analysis: An automatic contextual analysis and ensemble clustering approach and comparison
    Murtadha Talib AL-Sharuee, Fei Liu, Mahardhika Pratama · 2018 · Data & Knowledge Engineering
73. Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments
    Afzal Hussain Shahid, Maheshwari Prasad Singh · 2019 · Journal of Applied Biomedicine
74. Boosted Decision Trees and Applications
    Y. Coadou · 2013 · EPJ Web of Conferences
75. Extending Statistical Boosting
    Harald Binder, Olaf Gefeller, Matthias C. Schmid, Andreas J. Mayr · 2014 · Methods of Information in Medicine
76. Filter, Rank, and Transfer the Knowledge: Learning to Chat
    Sina Jafarpour · 2010
77. Online Importance Weight Aware Updates
    Nikos Karampatziakis, John C. Langford · 2010 · arXiv (Cornell University)
78. On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
    Jürgen Schmidhuber · 2015 · arXiv (Cornell University)
79. Comparisons of the prediction models for undiagnosed diabetes between machine learning versus traditional statistical methods
    Seong Gyu Choi, Minsuk Oh, Dong‐Hyuk Park, Byeongchan Lee, Yong‐ho Lee, Sun Ha Jee, Justin Y. Jeon · 2023 · Scientific Reports
80. Ensemble of single-layered complex-valued neural networks for classification tasks
    Md. Faijul Amin, Md. Monirul Islam, Kazuyuki Murase · 2009 · Neurocomputing
81. Prediction of company failure: Past, present and promising directions for the future
    Ranadeva Jayasekera · 2017 · International Review of Financial Analysis
82. Ensemble Learning-Based Rate-Distortion Optimization for End-to-End Image Compression
    Yefei Wang, Dong Liu, Siwei Ma, Feng Wu, Wen Gao · 2020 · IEEE Transactions on Circuits and Systems for Video Technology
83. Impact of the learners diversity and combination method on the generation of heterogeneous classifier ensembles
    María Paz Sesmero, José Antonio Iglesias, Elena Magán, Agapito Ledezma, Araceli Sanchis · 2021 · Applied Soft Computing
84. The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining
    Huy Nguyen Anh Pham, Evangelos Triantaphyllou · 2007 · Springer eBooks
85. Characterizing the sample complexity of private learners
    Amos Beimel, Kobbi Nissim B, Uri Stemmer · 2013 · Conference on Innovations in Theoretical Computer Science
86. Research on modulation recognition with ensemble learning
    Tong Liu, Guan Yanan, Yun Lin · 2017 · EURASIP Journal on Wireless Communications and Networking
87. Animal welfare and cow-calf contact-farmers’ attitudes, experiences and adoption barriers
    Bjørn Gunnar Hansen, Elise Langseth, Camilla Berge · 2022 · Journal of Rural Studies
88. Meta-learning approach to neural network optimization
    Pavel Kordík, Jan Koutník, Jan Drchal, Oleg Kovářík, Miroslav Čepek, Miroslav Šnorek · 2010 · Neural Networks
89. An Information-Extraction Approach to Speech Processing: Analysis, Detection, Verification, and Recognition
    Chin‐Hui Lee, Sabato Marco Siniscalchi · 2013 · Proceedings of the IEEE
90. On Developing a Driver Identification Methodology Using In-Vehicle Data Recorders
    Luís Moreira-Matias, Haneen Farah · 2017 · IEEE Transactions on Intelligent Transportation Systems
91. Micro-doppler based human-robot classification using ensemble and deep learning approaches
    Sherif Abdulatif, Qian Wei, Fady Aziz, Bernhard Kleiner, Urs Schneider · 2018 · IEEE Radar Conference (RadarConf)
92. Mapping machine-learned physics into a human-readable space
    Taylor Faucett, Jesse Thaler, Daniel Whiteson · 2021 · Physical review. D/Physical review. D.
93. Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield
    Niaz Muhammad Shahani, Xigui Zheng, Xiaowei Guo, Xin Wei · 2022 · Sustainability
94. Classification Trees and Rule-Based Models
    Max Kühn, Kjell Johnson · 2013 · Springer eBooks
95. Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping
    Zhu Liang, Changming Wang, Kaleem Ullah Jan Khan · 2020 · Stochastic Environmental Research and Risk Assessment
96. Relevant Examples and Relevant Features: Thoughts from Computational Learning Theory
    Avrim L. Blum · 1994
97. Boosting and Hard-Core Set Construction
    Adam R. Klivans, Rocco A. Servedio · 2003 · Machine Learning
98. Automatic facial emotion recognition
    Aitor Azcarate, Felix Hageloh, Koen Van De S, Roberto Valenti · 2005
99. Machine learning in geography–Past, present, and future
    Abigail V. Lavallin, Joni A. Downs · 2021 · Geography Compass
100. Design of ensemble neural network using the Akaike information criterion
     Zhiye Zhao, Yun Zhang, Hongjian Liao · 2008 · Engineering Applications of Artificial Intelligence