Boosting a Weak Learning Algorithm by Majority

Emotional state detection on mobility vehicle using camera: Feasibility and evaluation study

Tagnithammou, T., Monacelli, É., Ferszterowski, A., & Trénoras, L. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102419 · Cited by 13

Does model complexity add value to asset allocation? Evidence from machine learning forecasting models

Kynigakis, I., & Panopoulou, E. (2021) · Journal of Applied Econometrics · doi:10.1002/jae.2885 · Cited by 14

Forecasting emergency department admissions

Rocha, C. N., & Rodrigues, F. (2021) · Intelligent Data Analysis · doi:10.3233/ida-205390 · Cited by 13

Compositional modelling of immune response and virus transmission dynamics

Waites, W., Cavaliere, M., Danos, V., Datta, R., Eggo, R. M., Hallett, T. B., Manheim, D., Panovska‐Griffiths, J., Russell, T., & Zarnitsyna, V. I. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0307 · Cited by 16

Improving the performance of an unmixing model in sediment source apportionment using synthetic sediment mixtures and an adaptive boosting algorithm

Zhao, Y., Gao, G., Ding, G., Zhou, Q., Zhang, Y., Wang, J., & Zhou, J. (2022) · CATENA · doi:10.1016/j.catena.2022.106491 · Cited by 13

Boosting multivariate structured additive distributional regression models

Strömer, A., Klein, N., Staerk, C., Klinkhammer, H., & Mayr, A. J. (2023) · Statistics in Medicine · doi:10.1002/sim.9699 · Cited by 13

The AdaBoost Algorithm with the Imprecision Determine the Weights of the Observations

Burduk, R. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-05458-2_12 · Cited by 13

Tree-based identification of subgroups for time-varying covariate survival data

Bertolet, M. H., Brooks, M. M., & Bittner, V. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280212460442 · Cited by 13

Ensemble dimension reduction based on spectral disturbance for subspace clustering

Chen, X., Wang, Q., & Zhuang, S. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107182 · Cited by 13

Robust M-estimators and Machine Learning Algorithms for Improving the Predictive Accuracy of Seaweed Contaminated Big Data

Ibidoja, O. J., Shan, F. P., Mukhtar, M., Sulaiman, J., & Ali, M. K. M. (2023) · Journal of the Nigerian Society of Physical Sciences · doi:10.46481/jnsps.2023.1137 · Cited by 14

Continuous Experts and the Binning Algorithm

Abernethy, J., Langford, J. C., & Warmuth, M. K. (2006) · Lecture notes in computer science · doi:10.1007/11776420_40 · Cited by 13

Entropy-based knowledge spreading and application to mobility prediction

François, J.-M., & Leduc, G. (2005) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/1095921.1095924 · Cited by 13

A general dimension for query learning

Balcázar, J. L., Castro, J., Guijarro, D., Köbler, J., & Lindner, W. (2007) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2007.03.003 · Cited by 13

Applying Classification Trees to Analyze Electrical Contractors’ Accidents

Gholizadeh, P., & Esmaeili, B. (2016) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784479827.269 · Cited by 13

A selective approach to neural network ensemble based on clustering technology

Li, K., Huang, H., Ye, X.-C., & Cui, L. (2005) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2004.1378592 · Cited by 13

Hybrid intelligent systems for predictive toxicology - a distributed approach

Neagu, D. C., Crăciun, M., Stroia, S. A., & Bumbaru, S. (2005) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2005.52 · Cited by 13

Cost-Aware Robust Tree Ensembles for Security ApplicationsPreprint

Chen, Y., Wang, S., Jiang, W., Cidon, A., & Jana, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.01149 · Cited by 13

Improved freezing rain forecast using machine learning

Wen, Q. H., Wan, D., Dong, Q. A. G., Yan, Y., & Zhang, P. (2024) · Weather and Climate Extremes · doi:10.1016/j.wace.2024.100690 · Cited by 12

Development of improved deep learning models for multi-step ahead forecasting of daily river water temperature

Gheisari, M., Shafi, J., Kosari, S., Amanabadi, S., Mehdizadeh, S., Fernández‐Campusano, C., & Abdalla, H. B. (2025) · Engineering Applications of Computational Fluid Mechanics · doi:10.1080/19942060.2025.2450477 · Cited by 13

Towards an interpretable Rules Ensemble algorithm for classification in a categorical data space

Azmi, M., & Berrado, A. (2015) · International Conference on Intelligent Systems: Theories and Applications · doi:10.1109/sita.2015.7358390 · Cited by 12

Accelerating nearest neighbor partitioning neural network classifier based on CUDA

Wang, L., Zhu, X., Yang, B., Guo, J., Liu, S., Li, M., Zhu, J., & Abraham, A. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.10.023 · Cited by 12

Hardness-Aware Dictionary Learning: Boosting Dictionary for Recognition

Wang, L., Li, S., Wang, S., Kong, D., & Yin, B. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3017916 · Cited by 12

Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification

Liu, F., & Qian, Q. (2022) · Algorithms · doi:10.3390/a15050139 · Cited by 12

Log AdaBoost: Optimizing Polylog loss function to improve the generalization performance of AdaBoost

Lin, M., & Luo, H. (2022) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac57282.2022.10023814 · Cited by 12

Forecasting call center arrivals using temporal memory networks and gradient boosting algorithm

Chacón, H., Koppisetti, V., Hardage, D., Choo, K. R., & Rad, P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119983 · Cited by 14

Comparative Analysis of Feature Selection Methods for Blood Cell Recognition in Leukemia

Staroszczyk, T., Osowski, S., & Markiewicz, T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31537-4_37 · Cited by 12

Lesion detection of electronic gastroscope images based on multiscale texture feature

Shen, X. Q., Sun, K., Zhang, S., & Cheng, S. (2012) · IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) · doi:10.1109/icspcc.2012.6335638 · Cited by 12

Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization

Veeramachaneni, K. K., Vladislavleva, E., & O’Reilly, U.-M. (2012) · Genetic Programming and Evolvable Machines · doi:10.1007/s10710-011-9153-2 · Cited by 12

A traffic surveillance system using dynamic saliency map and SVM boosting

Woo, J.-W., Lee, W., & Lee, M. (2010) · International Journal of Control Automation and Systems · doi:10.1007/s12555-010-0503-2 · Cited by 12

Circular ECOC. A theoretical and experimental analysis

Ghaderi, R., & Windeau, T. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2000.906048 · Cited by 12

An approach for deciphering patient-specific variations with application to breast cancer molecular expression profiles

Nagarajan, R., & Upreti, M. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.07.022 · Cited by 12

Investigation of protein quaternary structure via stoichiometry and symmetry ınformation

Korkmaz, S., Duarte, J. M., Prlić, A., Göksülük, D., Zararsız, G., Saraçbaşı, O., Burley, S. K., & Rose, P. W. (2018) · PLoS ONE · doi:10.1371/journal.pone.0197176 · Cited by 12

An Entropy-Based Machine Learning Algorithm for Combining Macroeconomic Forecasts

Bretó, C., Espinosa, P., Hernández, P., & Pavía, J. M. (2019) · Entropy · doi:10.3390/e21101015 · Cited by 12

High-Dimensional Statistics

Fan, J. (2012) · Selected works in probability and statistics · doi:10.1007/978-1-4614-5544-8_7 · Cited by 12

Improving Intrusion Detection System Using Ensemble Methods and Over-Sampling Technique

Li, F., Ma, W., Li, H., & Li, J. (2022) · International Academic Exchange Conference on Science and Technology Innovation (IAECST) · doi:10.1109/iaecst57965.2022.10062178 · Cited by 12

Empirical Study on Weighted Voting Multiple Classifiers

Sun, Y., Kamel, M. S., & Wong, A. K. C. (2005) · Lecture notes in computer science · doi:10.1007/11551188_36 · Cited by 12

On the Existence of Linear Weak Learners and Applications to Boosting

Mannor, S., & Meir, R. (2002) · Machine Learning · doi:10.1023/a:1013959922467 · Cited by 12

Multi-objective evolution of the Pareto optimal set of neural network classifier ensembles

Engen, V., Vincent, J., Schierz, A. C., & Phalp, K. T. (2009) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2009.5212485 · Cited by 12

Every Linear Threshold Function has a Low-Weight Approximator

Servedio, R. A. (2006) · IEEE Conference on Computational Complexity (CCC) · doi:10.1109/ccc.2006.18 · Cited by 12

Multi-class Adaboost Algorithm Based on the Adjusted Weak Classifier

Yang, X. (2016) · Cited by 12

A proposal for applying pdi-Boosting to brain-computer interfaces

Hayashi, I., Tsuruse, S., Suzuki, J., & Kozma, R. (2012) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2012.6251152 · Cited by 12

Stochastic Attribute Selection Committees with Multiple Boosting: Learning More Accurate and More Stable Classifier Committees

Zheng, Z., & Webb, G. I. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48912-6_17 · Cited by 12

Quantum BoostingPreprint

Arunachalam, S., & Maity, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05056 · Cited by 12

Factors That Can Affect Model Performance

Kühn, M., & Johnson, K. (2013) · Springer eBooks · doi:10.1007/978-1-4614-6849-3_20 · Cited by 11

Data‐driven models for predicting remaining useful life of high‐speed shaft bearings in wind turbines using vibration signal analysis and sparrow search algorithm

Pandit, R., & Xie, W. (2023) · Energy Science & Engineering · doi:10.1002/ese3.1597 · Cited by 11

Ensemble methods for testing a global null

Liu, Y., Liu, Z., & Lin, X. (2023) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1093/jrsssb/qkad131 · Cited by 11

Quantifying the sources of suspended sediments in the Nianchu River Basin, southern Tibetan Plateau: Evaluation and application of a semiempirical fingerprinting framework

Zhao, Y., Zhang, F., Zeng, C., Shi, X., Kong, J., & Xie, S. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.130711 · Cited by 11

A machine learning framework for predicting the axial capacity of cold-formed steel face-to-face built-up channel sections at elevated temperatures

Tang, P., Dai, Y., Lu, C., & Hu, S. (2024) · Structures · doi:10.1016/j.istruc.2024.107144 · Cited by 11

Improving deep neural network ensembles using reconstruction error

Huang, W., Hong, H., Bian, K., Zhou, X., Song, G., & Xie, K. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280524 · Cited by 11

Encoding Through Patterns: Regression Tree–Based Neuronal Population Models

Haslinger, R., Pipa, G., Lewis, L. D., Nikolić, D., Williams, Z. M., & Brown, E. N. (2013) · Neural Computation · doi:10.1162/neco_a_00464 · Cited by 11

Boosting-Based System Combination for Machine Translation

Xiao, T., Zhu, J., Zhu, M., & Wang, H. (2010) · Cited by 11

Teaching and Compressing for Low VC-Dimension

Moran, S., Shpilka, A., Wigderson, A., & Yehudayoff, A. (2017) · Springer eBooks · doi:10.1007/978-3-319-44479-6_26 · Cited by 11

Learning DNFs under product distributions via mu--biased quantum Fourier sampling

Kanade, V., Rocchetto, A., & Simone Severini, B. (2019) · Quantum Information and Computation · doi:10.26421/qic19.15-16-1 · Cited by 11

Boosting for Correlated Binary Classification

Adewale, A. J., Dinu, I. A., & Yasui, Y. (2010) · Journal of Computational and Graphical Statistics · doi:10.1198/jcgs.2009.07118 · Cited by 11

EVALUATION OF SELECTED FEATURES FOR CAR DETECTION IN AERIAL IMAGES

Tuermer, S., Leitloff, J., Reinartz, P., & Stilla, U. (2012) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsarchives-xxxviii-4-w19-341-2011 · Cited by 11

SmartDog: Real-Time Detection of Smartphone Theft

Chang, S., Lu, T., & Song, H. (2016) · 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/ithings-greencom-cpscom-smartdata.2016.61 · Cited by 11

BoostVHT

Vasiloudis, T., Beligianni, F., & Morales, G. D. F. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132974 · Cited by 11

Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks

Li, M., & Wang, S. (2019) · Energies · doi:10.3390/en12173256 · Cited by 11

Simple Lifelong Learning Machines

Vogelstein, J. T., Dey, J. K., Helm, H. S., LeVine, W., Mehta, R., Tomita, T. M., Xu, H., Geisa, A., Wang, Q., van de Ven, G. M., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3595364 · Cited by 26

Identifying High-Dimensional Biomarkers for Personalized Medicine via Variable Importance Ranking

Baek, S., Moon, H., Ahn, H., Kodell, R. L., Lin, C.-J., & Chen, J. J. (2008) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543400802278023 · Cited by 11

Experiments with Two New Boosting Algorithms

Sun, X., & Zhou, H. (2010) · Intelligent Information Management · doi:10.4236/iim.2010.26047 · Cited by 11

Genetic Algorithm Based Optimization for AdaBoost

Zhang, D., & Yang, K. (2008) · doi:10.1109/csse.2008.1040 · Cited by 11

Combination of boosting and discriminative training for natural language call steering systems

Zitouni, I., Kuo, H.-K. J., & Lee, C. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.1005666 · Cited by 11

Learning with queries corrupted by classification noise

Jackson, J. C., Shamir, E., & Shwartzman, C. (2002) · Israel Symposium on Theory and Computing Systems (ISTCS) · doi:10.1109/istcs.1997.595156 · Cited by 11

A NanoFE simulation-based surrogate machine learning model to predict mechanical functionality of protein networks from live confocal imaging

Asgharzadeh, P., Birkhold, A., Trivedi, Z., Özdemir, B., Ralf Reski, A., & Röhrle, O. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.09.024 · Cited by 11

Causal Gradient Boosting: Boosted Instrumental Variable Regression

Bakhitov, E., & Singh, A. (2022) · ACM Conference on Economics and Computation (EC) · doi:10.1145/3490486.3538251 · Cited by 11

Decision Tree Classification of Spatial Data Patterns From Videokeratography Using Zernike Polynomials

Twa, M. D., Parthasarathy, S., Raasch, T. W., & Bullimore, M. A. (2003) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972733.1 · Cited by 11

Committee polyhedral separability: complexity and polynomial approximation

Khachay, M. (2015) · Machine Learning · doi:10.1007/s10994-015-5505-0 · Cited by 11

American Option Pricing: A Classification — Monte Carlo (CMC) Approach

Picazo, J. A. (2002) · Springer eBooks · doi:10.1007/978-3-642-56046-0_29 · Cited by 11

A DGC-Based Data Classification Method Used for Abnormal Network Intrusion Detection

Yang, B., Peng, L., Chen, Y., Liu, H., & Yuan, R. (2006) · Lecture notes in computer science · doi:10.1007/11893295_24 · Cited by 11

Leveraged Vector Machines

Singer, Y. (1999) · Cited by 11

Orthogonal Decision Trees

Kargupta, H., & Dutta, H. (2005) · Proceedings · doi:10.1109/icdm.2004.10072 · Cited by 11

iBoost: Boosting Using an instance-Based Exponential Weighting Scheme

Kwek, S. S., & Nguyen, C. D. M. (2002) · Lecture notes in computer science · doi:10.1007/3-540-36755-1_21 · Cited by 11

Text Categorization Using Transductive Boosting

Taira, H., & Haruno, M. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44795-4_39 · Cited by 11

Normal mammogram analysis

Sun, Y., & Delp, E. J. (2004) · Purdue e-Pubs (Purdue University System) · Cited by 11

Técnicas de extracción de características y clasificación de imágenes orientada a objetos aplicadas a la actualización de bases de datos de ocupación del suelo

Recio, J. A. (2009) · doi:10.4995/thesis/10251/6848 · Cited by 11

Building reliable metaclassifiers for text learning

Carbonell, J., Lafferty, J., & Bennett, P. N. (2006) · Cited by 11

Proximal boosting: Aggregating weak learners to minimize non-differentiable losses

Fouillen, E., Boyer, C., & Sangnier, M. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.065 · Cited by 10

Surface defect detection and semantic segmentation with a novel lightweight deep neural network

Huang, Q., Li, F., Yang, Y., Tao, X., Li, W., Xu, W., & Wang, Y. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad4ab2 · Cited by 10

Expert-level detection of M-proteins in serum protein electrophoresis using machine learning

Elfert, E., Kaminski, W. E., Matek, C., Hoermann, G., Axelsen, E. W., Marr, C., & Piehler, A. P. (2024) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2024-0222 · Cited by 10

Optimising Venturi flume oxygen transfer efficiency using uncertainty-aware decision trees

Tiwari, N. K., & Panwar, D. (2024) · Water Science & Technology · doi:10.2166/wst.2024.393 · Cited by 10

Ada-boosting Extreme learning machines for handwritten digit and digit strings recognition

Saabni, R. M. (2015) · International Conference on Digital Information Processing and Communications · doi:10.1109/icdipc.2015.7323034 · Cited by 10

A Face Detection System Based Skin Color and Neural Network

Wang, Z.-J., & Tong, L. (2008) · International Conference on Computer Science and Software Engineering · doi:10.1109/csse.2008.586 · Cited by 12

Introducing an intelligent algorithm for extraction of sand dunes from Landsat satellite imagery in terrestrial and coastal environments

Mohammadpoor, M., & Eshghizadeh, M. (2021) · Journal of Coastal Conservation · doi:10.1007/s11852-020-00789-x · Cited by 10

Warpage optimization of the GFRP injection molding process parameters

Liu, X., Fan, X., Guo, Y., Man, B., & Li, L. (2021) · Microsystem Technologies · doi:10.1007/s00542-021-05241-0 · Cited by 10

A support vector machines framework for identification of infrared spectra

Chowdhury, M. A. Z., Rice, T. E., & Oehlschlaeger, M. A. (2022) · Applied Physics B · doi:10.1007/s00340-022-07879-8 · Cited by 10

Estimation and comparison of gabion weir oxygen mass transfer by ensemble learnings of bagging, boosting, and stacking algorithms

Luxmi, K., Tiwari, N. K., & Ranjan, S. (2023) · ISH Journal of Hydraulic Engineering · doi:10.1080/09715010.2023.2203109 · Cited by 10

Self-Adaptive Ensemble-based Approach for Software Effort Estimation

Shukla, S., & Kumar, S. (2023) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner56733.2023.00060 · Cited by 10

Clear Water Scour Depth Prediction using Gradient Boosting Machine and Deep Learning

Singh, B., & Minocha, V. K. (2024) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1327/1/012030 · Cited by 10

Multi-view fast object detection by using extended haar filters in uncontrolled environments

Vural, S., Mae, Y., Uvet, H., & Arai, T. (2012) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.10.004 · Cited by 12

Ensemble learning from multiple information sources via label propagation and consensus

Lin, Y., Hu, X., & Wu, X. (2014) · Applied Intelligence · doi:10.1007/s10489-013-0508-7 · Cited by 10

Ear Detection Based on Improved AdaBoost Algorithm

Li, W., & Mu, Z. (2008) · Chinese Conference on Pattern Recognition · doi:10.1109/ccpr.2008.69 · Cited by 10

Distribution‐free inference on contrasts of arbitrary summary measures of survival

Rudser, K. D., LeBlanc, M. L., & Emerson, S. S. (2012) · Statistics in Medicine · doi:10.1002/sim.4505 · Cited by 10

Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters

Goyal, A., Morvant, E., Germain, P., & Amini, M.-R. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.04.072 · Cited by 10

Rule extraction using ensemble of neural network ensembles

Chakraborty, M., Biswas, S. K., & Purkayastha, B. (2022) · Cognitive Systems Research · doi:10.1016/j.cogsys.2022.07.004 · Cited by 10

Polyphonic Music Information Retrieval Based on Multi-label Cascade Classification System

Jiang, W., Cohen, A., & Raś, Z. W. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-04141-9_6 · Cited by 10

A reduction technique for nearest-neighbor classification: Small groups of examples

Kubát, M., & Cooperson, M. (2001) · Intelligent Data Analysis · doi:10.3233/ida-2001-5603 · Cited by 10

Hierarchical distributed data classification inwireless sensor networks

Cheng, X., Ji, X., Pei, J., & Liu, J. (2009) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mobhoc.2009.5336999 · Cited by 10

Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning

Gavinsky, D. (2002) · Lecture notes in computer science · doi:10.1007/3-540-36169-3_10 · Cited by 10

Speaker recognition using adaptively boosted classifier

Foo, S. W., & Lim, E. G. (2002) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2001.949632 · Cited by 10

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