Boosting a Weak Learning Algorithm by Majority

An integrated approach to emotion recognition and gender classification

Rao, K. P., Rao, M. V. P. C. S., & Chowdary, N. H. (2019) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.03.002 · Cited by 25

Non-iterative Knowledge Fusion in Deep Convolutional Neural Networks

Leontev, M. I., Islenteva, V., & Sukhov, S. V. (2019) · Neural Processing Letters · doi:10.1007/s11063-019-10074-0 · Cited by 24

Argumentation based reinforcement learning for meta-knowledge extraction

Xu, J., Yao, L., Li, L., Ji, M., & Tang, G. (2019) · Information Sciences · doi:10.1016/j.ins.2019.07.094 · Cited by 24

On the Power of Small-Depth Computation

Viola, E. (2009) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000033 · Cited by 24

Using Data Mining Techniques in Fiscal Fraud Detection

Bonchi, F., Giannotti, F., Mainetto, G., & Pedreschi, D. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48298-9_39 · Cited by 24

Orthogonal Nonlinear Partial Least-Squares Regression

Doymaz, F., Palazoğlu, A., & Romagnoli, J. A. (2003) · Industrial & Engineering Chemistry Research · doi:10.1021/ie0109051 · Cited by 24

Upper bounds for error rates of linear combinations of classifiers

Murua, A. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000235 · Cited by 24

Robust Regression by Boosting the Median

Kégl, B. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45167-9_20 · Cited by 24

Learning DNF by approximating inclusion-exclusion formulae

Tarui, J., & Tsukiji, T. (2003) · IEEE Conference on Computational Complexity (CCC) · doi:10.1109/ccc.1999.766279 · Cited by 24

A genetic algorithm for pattern recognition analysis of pyrolysis gas chromatographic data

Lavine, B. K., Moores, A. J., & Helfend, L. K. (1999) · Journal of Analytical and Applied Pyrolysis · doi:10.1016/s0165-2370(99)00002-9 · Cited by 24

An effective semi-cross-validation model selection method for extreme learning machine with ridge regression

Shao, Z., Er, M. J., & Wang, N. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.10.002 · Cited by 23

A Superpixel Guided Sample Selection Neural Network for Handling Noisy Labels in Hyperspectral Image Classification

Xu, H., Zhang, H., & Zhang, L. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3040879 · Cited by 23

A review on various methods for recognition of urine particles using digital microscopic images of urine sediments

Suhail, K., & Brindha, D. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102806 · Cited by 23

Lightweight Transmission Line Fault Detection Method Based on Leaner YOLOv7-Tiny

Wang, Q., Zhang, Z., Chen, Q., Zhang, J., & Kang, S. (2024) · Sensors · doi:10.3390/s24020565 · Cited by 23

Alert Correlation Using Correlation Probability Estimation and Time Windows

Ahmadinejad, S. H., & Jalili, S. (2009) · International Conference on Computer Technology and Development · doi:10.1109/icctd.2009.22 · Cited by 23

Robust servo-control for underwater robots using banks of visual filters

Sattar, J., & Dudek, G. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152197 · Cited by 23

Facial Expression Recognition Based on t-SNE and AdaboostM2

Yi, J., Mao, X., Xue, Y., & Compare, A. (2013) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/greencom-ithings-cpscom.2013.321 · Cited by 23

Genetic Programming Approaches for Solving Elliptic Partial Differential Equations

Sóbester, A., Nair, P. B., & Keane, A. J. (2008) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.908467 · Cited by 23

Earthquake Prediction by RBF Neural Network Ensemble

Liu, Y., Wang, Y., Li, Y., Zhang, B., & Wu, G. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-28648-6_153 · Cited by 23

Data-Fusion Techniques for Open-Set Recognition Problems

Córdova, M., Júnior, P. R. M., Rocha, A. D. R., & Torres, R. D. S. (2018) · IEEE Access · doi:10.1109/access.2018.2824240 · Cited by 23

Improved Vine Copula-Based Dependence Description for Multivariate Process Monitoring Based on Ensemble Learning

Zhou, Y., Li, S., & Xiong, N. (2019) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.8b04081 · Cited by 23

Automatic Wireless Signal Classification in Multimedia Internet of Things: An Adaptive Boosting Enabled Approach

Wahla, A. H., Chen, L., Wang, Y., Chen, R., & Wu, F. (2019) · IEEE Access · doi:10.1109/access.2019.2950989 · Cited by 33

Using the Pseudo-Dimension to Analyze Approximation Algorithms for Integer Programming

Long, P. M. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44634-6_4 · Cited by 23

Seeing Inside the Black Box: Using Diffusion Index Methodology to Construct Factor Proxies in Large Scale Macroeconomic Time Series Environments

Armah, N. A., & Swanson, N. R. (2010) · Econometric Reviews · doi:10.1080/07474938.2010.481549 · Cited by 23

A Geometric Approach to Leveraging Weak Learners

Duffy, N., & Helmbold, D. P. (1999) · Lecture notes in computer science · doi:10.1007/3-540-49097-3_3 · Cited by 23

Labeled Compression Schemes for Extremal Classes

Moran, S., & Warmuth, M. K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46379-7_3 · Cited by 23

Concept lattice based composite classifiers for high predictability

Xie, Z., Hsu, W., Liu, Z., & Lee, M. L. (2002) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/09528130210164206 · Cited by 23

A comparative study on model selection and multiple model fusion

Chen, H., & Huang, S. (2005) · International Conference on Information Fusion (FUSION) · doi:10.1109/icif.2005.1591938 · Cited by 23

Bankruptcy prediction using optimal ensemble models under balanced and imbalanced data

Amirshahi, B., & Lahmiri, S. (2024) · Expert Systems · doi:10.1111/exsy.13599 · Cited by 22

Combining Ensemble of Classifiers by Using Genetic Programming for Cyber Security Applications

Gianluigi Folino, A., & Pisani, F. S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16549-3_5 · Cited by 22

Fully corrective gradient boosting with squared hinge: Fast learning rates and early stopping

Zeng, J., Zhang, M., & Lin, S.-B. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.12.016 · Cited by 22

Optimized parallel implementation of face detection based on GPU component

Chouchene, M., Sayadi, F. E., Bahri, H., Dubois, J., Mitéran, J., & Atri, M. (2015) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2015.04.009 · Cited by 22

Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis

Alizamir, M., Wang, M., Ikram, R. M. A., Kim, S., Ahmed, K. O., & Heddam, S. (2024) · Engineering Applications of Computational Fluid Mechanics · doi:10.1080/19942060.2024.2422060 · Cited by 22

A pattern fusion model for multi-step-ahead CPU load prediction

Yang, D., Cao, J., Fu, J., Wang, J., & Guo, J. (2012) · Journal of Systems and Software · doi:10.1016/j.jss.2012.12.023 · Cited by 22

The robustness of majority voting compared to filtering misclassified instances in supervised classification tasks

Smith, M. R., & Martinez, T. R. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9518-2 · Cited by 22

Non interactive simulation of correlated distributions is decidable

De, A., Mossel, E., & Neeman, J. (2018) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611975031.174 · Cited by 22

Prediction of pork storage time using Fourier transform near infrared spectroscopy and Adaboost‐ULDA

Wu, X., Fu, H., Tian, X., Wu, B., & Sun, J. (2017) · Journal of Food Process Engineering · doi:10.1111/jfpe.12566 · Cited by 22

Online Learning with a Hint

Dekel, O., Flajolet, A., Haghtalab, N., & Jaillet, P. (2017) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 22

Indistinguishability by Adaptive Procedures with Advice, and Lower Bounds on Hardness Amplification Proofs

Grinberg, A., Shaltiel, R., & Viola, E. (2018) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2018.00094 · Cited by 22

Adaptive MultiScale Segmentations for Hyperspectral Image Classification

Leng, Q., Yang, H., Jiang, J., & Tian, Q. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2971716 · Cited by 22

Distributed, Collaborative Data Analysis from Heterogeneous Sites Using a Scalable Evolutionary Technique

Park, B., Kargupta, H., Johnson, E. W., Sanseverino, E. R., Hershberger, D. E., & Silvestre, L. (2002) · Applied Intelligence · doi:10.1023/a:1012813326519 · Cited by 22

Boosting and combination of classifiers for natural language call routing systems

Zitouni, I., Kuo, H.-K. J., & Lee, C. (2003) · Speech Communication · doi:10.1016/s0167-6393(03)00103-1 · Cited by 22

Building an Ensemble of Fine-Tuned Naive Bayesian Classifiers for Text Classification

Hindi, K. M. E., AlSalman, H., Qasem, S., & Al-Ahmadi, S. A. (2018) · Entropy · doi:10.3390/e20110857 · Cited by 22

On Attribute Efficient and Non-adaptive Learning of Parities and DNF Expressions

Feldman, V. (2005) · Lecture notes in computer science · doi:10.1007/11503415_39 · Cited by 22

Combatting the mismatch: Modeling bike-sharing rental and return machine learning classification forecast in Seoul, South Korea

Choi, S. J., Jiao, J., Lee, H. K., & Farahi, A. (2023) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2023.103587 · Cited by 21

A weakly supervised approach for object detection based on Soft-Label Boosting

Wang, W., Wang, Y., Chen, F., & Sowmya, A. (2013) · Proceedings · doi:10.1109/wacv.2013.6475037 · Cited by 21

Shrinkage Degree in L₂-Rescale Boosting for Regression

Xu, L., Lin, S.-B., Wang, Y., & Xu, Z. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2560224 · Cited by 21

Tree-Based Methods and Their Applications

Lin, N., Noe, D. A., & He, X. (2006) · Springer eBooks · doi:10.1007/978-1-84628-288-1_30 · Cited by 21

Urine Sediment Recognition Method Based on SVM and AdaBoost

Shen, M.-L., & Zhang, R. (2009) · International Conference on Computational Intelligence and Software Engineering (CiSE) · doi:10.1109/cise.2009.5365881 · Cited by 21

Bounding the generalization error of convex combinations of classifiers: balancing the dimensionality and the margins

Koltchinskii, V. I., Panchenko, D., & Lozano, F. (2003) · The Annals of Applied Probability · doi:10.1214/aoap/1042765667 · Cited by 21

Some New Bounds on the Generalization Error of Combined Classifiers

Koltchinskii, V. I., Panchenko, D., & Lozano, F. (2000) · Cited by 21

MapReduce network enabled algorithms for classification based on association rules

Hammoud, S. (2011) · Brunel University Research Archive (BURA) (Brunel University London) · Cited by 21

Decision Tree Learning

Podgorelec, V., & Zorman, M. (2015) · Encyclopedia of Complexity and Systems Science · doi:10.1007/978-3-642-27737-5_117-2 · Cited by 21

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Ikeagwuani, C. C., & Nwonu, D. C. (2022) · Innovative Infrastructure Solutions · doi:10.1007/s41062-022-00875-z · Cited by 20

Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping

Lin, S.-B., Lei, Y., & Zhou, D. (2019) · The HKU Scholars Hub (University of Hong Kong) · Cited by 20

A gradient boosting approach with diversity promoting measures for the ensemble of surrogates in engineering

Queipo, N. V., & Nava, E. (2019) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-019-02325-4 · Cited by 20

Automated detection of glaucoma using optical coherence tomography angiogram images

Chan, Y. M., Ng, E. Y. K., Jahmunah, V., Koh, J. E. W., Lih, O. S., Leon, L. Y. W., & Acharya, U. R. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103483 · Cited by 20

An Ensemble of Computational Intelligence Models for Software Maintenance Effort Prediction

Aljamaan, H., Elish, M. O., & Ahmad, I. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38679-4_60 · Cited by 20

Ensembles of biased classifiers

Khoussainov, R., Heß, A., & Kushmerick, N. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102405 · Cited by 20

On the fusion of threshold classifiers for categorization and dimensionality reduction

Kestler, H. A., Lausser, L. M., Lindner, W., & Palm, G. (2011) · Computational Statistics · doi:10.1007/s00180-011-0243-7 · Cited by 20

Using machine learning to cope with imbalanced classes in natural speech: evidence from sentence boundary and disfluency detection

Liu, Y., Shriberg, E. E., Stolcke, A., & Harper, M. P. (2004) · Interspeech (USB) · doi:10.21437/interspeech.2004-573 · Cited by 20

Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network

Asghar, N., Khalil, U., Ahmad, B., Alshanbari, H. M., Hamraz, M., Ahmad, B., & Khan, D. M. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02525-z · Cited by 19

Gradient boosting trees with Bayesian optimization to predict activity from other geotechnical parameters

Díaz, E., & Spagnoli, G. (2023) · Marine Georesources and Geotechnology · doi:10.1080/1064119x.2023.2251025 · Cited by 19

A new intelligent charging strategy in a stationary hydrogen energy-based power plant for optimal demand side management of plug-in EVs

Çakmak, R., Meral, H., & Bayrak, G. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.02.132 · Cited by 19

Application of Combined Classifiers to Data Stream Classification

Woźniak, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40925-7_2 · Cited by 19

Landscapes of Naïve Bayes classifiers

Hoare, Z. (2007) · Pattern Analysis and Applications · doi:10.1007/s10044-007-0079-5 · Cited by 19

Quantitative Determination of the Components in Corn and Tobacco Samples by Using Near-Infrared Spectroscopy and Multiblock Partial Least Squares

Ming, J., Cai, W., & Shao, X. (2010) · Analytical Letters · doi:10.1080/00032711003686973 · Cited by 19

Coal Exploration Based on a Multilayer Extreme Learning Machine and Satellite Images

Le, B. T., Xiao, D., Mao, Y., He, D., Zhang, S., Sun, X., & Liu, X. (2018) · IEEE Access · doi:10.1109/access.2018.2860278 · Cited by 20

Concurrent optimization of multiple base learners in neural network ensembles: An adaptive niching differential evolution approach

Huang, T., Duan, D.-T., Gong, Y., Ye, L., Ng, W. W. Y., & Zhang, J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.020 · Cited by 19

Binary Classifiers for Data Integrity Detection in Wearable IoT Edge Devices

John, A., Panicker, R. C., Cardiff, B., Lian, Y., & John, D. (2020) · IEEE Open Journal of Circuits and Systems · doi:10.1109/ojcas.2020.3009520 · Cited by 19

Predicting Chaotic Time Series by Boosted Recurrent Neural Networks

Assaad, M. A., Boné, R., & Cardot, H. (2006) · Lecture notes in computer science · doi:10.1007/11893257_92 · Cited by 19

INDUCTION FROM MULTI-LABEL EXAMPLES IN INFORMATION RETRIEVAL SYSTEMS: A CASE STUDY

Sarinnapakorn, K., & Kubát, M. (2008) · Applied Artificial Intelligence · doi:10.1080/08839510801972827 · Cited by 19

A Kernel Canonical Correlation Analysis Based Idle-State Detection Method for SSVEP-Based Brain-Computer Interfaces

Zhang, Z. M., & Deng, Z. (2011) · Advanced materials research · doi:10.4028/www.scientific.net/amr.341-342.634 · Cited by 19

Dropout and DropConnect based Ensemble of Random Vector Functional Link Neural Network

Katuwal, R., & Suganthan, P. N. (2018) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2018.8628640 · Cited by 19

Combining Classifiers Using Correspondence Analysis

Merz, C. J. (1997) · Cited by 19

A Primal-Dual Convergence Analysis of BoostingPreprint

TelgarskyMatus (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1101.4752 · Cited by 19

Evaluation of Cost-Sensitive Learning Models in Forecasting Business Failure of Capital Market Firms

Peykani, P., Peymany, M., Tănăsescu, C. R., Sargolzaei, M., & Kamyabfar, H. (2025) · Mathematics · doi:10.3390/math13030368 · Cited by 22

Improving Sentiment Classification Using Feature Highlighting and Feature Bagging

Dai, L., Chen, H., & Li, X. (2011) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2011.96 · Cited by 18

Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification

Leng, Q., Yang, H., & Jiang, J. (2019) · Remote Sensing · doi:10.3390/rs11091116 · Cited by 18

Learning from data with uncertain labels by boosting credal classifiers

Quost, B., & Denœux, T. (2009) · doi:10.1145/1610555.1610561 · Cited by 18

Object Tracking Method Based on SURF

Shuo, H., Na, W., & Huajun, S. (2012) · AASRI Procedia · doi:10.1016/j.aasri.2012.11.055 · Cited by 18

Exploiting diversity for optimizing margin distribution in ensemble learning

Hu, Q., Li, L., Wu, X., Schaefer, G., & Yu, D. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.06.005 · Cited by 19

Combining Classifier Based on Decision Tree

Yao, Y., Fu, Z., Zhao, X., & Wen-fang, C. (2009) · WASE International Conference on Information Engineering · doi:10.1109/icie.2009.12 · Cited by 18

On PAC learning algorithms for rich Boolean function classes

Hellerstein, L., & Servedio, R. A. (2007) · Theoretical Computer Science · doi:10.1016/j.tcs.2007.05.018 · Cited by 18

Logical Analysis of Data as a tool for the analysis of Probabilistic Discrete Choice Behavior

Bruni, R., Bianchi, G., Dolente, C., & Leporelli, C. (2018) · Computers & Operations Research · doi:10.1016/j.cor.2018.04.014 · Cited by 18

An Effective Recursive Technique for Multi-Class Classification and Regression for Imbalanced Data

Alam, T., Ahmed, C. F., Zahin, S. A., Khan, M. A., & Islam, M. T. (2019) · IEEE Access · doi:10.1109/access.2019.2939755 · Cited by 18

Performance Analysis of Classification Algorithms on Birth Dataset

Abbas, S. A., Rehman, A. U., Majeed, F., Majid, A., Malik, M. S. A., Kazmi, Z. H., & Zafar, S. (2020) · IEEE Access · doi:10.1109/access.2020.2999899 · Cited by 19

Classifying Cannabis Sativa Flowers, Stems and Leaves using Statistical Machine Learning with Near-Infrared Hyperspectral Reflectance Imaging

Holmes, W. S., Ooi, M. P., Kuang, Y. C., Simpkin, R., Lopez-Ubiria, I., Vidiella, A., Blanchon, D., Gupta, G. S., & Demidenko, S. N. (2020) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc43012.2020.9129531 · Cited by 18

Boosting in the presence of noise

Kalai, A. T., & Servedio, R. A. (2003) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/780542.780573 · Cited by 18

The generalized Bayesian committee machine

Tresp, V. (2000) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/347090.347118 · Cited by 18

Noise peeling methods to improve boosting algorithms

Martinez, W. G., & Gray, J. B. (2015) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2015.06.010 · Cited by 18

Building Classification Models from Microarray Data with Tree-Based Classification Algorithms

Tan, P., Dowe, D. L., & Dix, T. I. (2007) · Springer eBooks · doi:10.1007/978-3-540-76928-6_60 · Cited by 18

Fast Adaboost Training Algorithm by Dynamic Weight Trimming

Jia, H.-X., & Zhang, Y. (2009) · Chinese Journal of Computers · doi:10.3724/sp.j.1016.2009.00336 · Cited by 18

New results in breast cancer classification obtained from an evolutionary computation/adaptive boosting hybrid using mammogram and history data

Land, W. H., Masters, T. D., Lo, J. Y.-C., McKee, D. W., & Anderson, F. R. (2002) · IEEE Conference on Soft Computing in Industrial Applications (SMCia) · doi:10.1109/smcia.2001.936727 · Cited by 18

New Boosting Algorithms for Classification Problems with Large Number of Classes Applied to a Handwritten Word Recognition Task

Günter, S., & Horst Bunke, A. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44938-8_33 · Cited by 18

A geometric approach to leveraging weak learners

Duffy, N., & Helmbold, D. P. (2002) · Theoretical Computer Science · doi:10.1016/s0304-3975(01)00083-4 · Cited by 18

A Novel Classification Method Based on Data Gravitation

Peng, L., Chen, Y., Yang, B. S., & Chen, Z. (2006) · International Conference on Neural Networks and Brain · doi:10.1109/icnnb.2005.1614719 · Cited by 18

Application of Neural Networks In Image Processing and Visualization

Cristea, P. D. (2009) · NATO science for peace and security series. C, Environmental security · doi:10.1007/978-90-481-2899-0_5 · Cited by 18

On the rate of convergence of regularized boosting classifiers

Blanchard, G., Lugosi, G., & Vayatis, N. (2003) · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · doi:10.1162/1532443041424319 · Cited by 18

Discussion of the paper "Arcing Classifiers" by Leo Breiman

Freund, Y., & Schapire, R. E. (1998) · Cited by 18

An improved boosting algorithm and its implications on learning complexity

Freund, Y. (1992) · Conference on Learning Theory · doi:10.1145/130385.130429 · Cited by 61

The Strength of Weak Learnability

Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600

Boosting Simple Learners

Alon, N., Gonen, A., Hazan, E., & Moran, S. (2021) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3406325.3451030 · Cited by 11

The learning of a class of n-dimensional Boolean functions with a two input perceptron using the boosting algorithm

Fortino, G. (1997) · Archive of Doctoral Theses and Digital Collections (University of Calabria)

Perceptron, Winnow, and PAC Learning

Servedio, R. A. (2002) · SIAM Journal on Computing · doi:10.1137/s0097539798340928 · Cited by 8