Boosting a Weak Learning Algorithm by Majority

A Drifting-Games Analysis for Online Learning and Applications to BoostingPreprint

Luo, H., & Schapire, R. E. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.1856 · Cited by 18

LEARNING ACCURATE AND UNDERSTANDABLE RULES FROM SVM CLASSIFIERS

Chen, F. (2004) · Summit (Simon Fraser University) · Cited by 18

Multimodal behavioral and physiological signals as indicators of cognitive load

Zhou, J., Yu, K., Chen, F., Wang, Y., & Arshad, S. Z. (2018) · The Handbook of Multimodal-Multisensor Interfaces: Foundations, User Modeling, and Common Modality Combinations - Volume 2 · doi:10.1145/3107990.3108002 · Cited by 25

Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China

Liu, L., Danish, A., Wang, X., & Zhu, W. (2024) · Geocarto International · doi:10.1080/10106049.2024.2326005 · Cited by 17

A dual evolutionary bagging for class imbalance learning

Guo, Y., Feng, J., Jiao, B., Cui, N., Yang, S., & Yu, Z. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117843 · Cited by 17

COVID-19 chest X-ray image classification in the presence of noisy labels

Xiao-qing, Y. I. N. G., Liu, H., & Huang, R. (2023) · Displays · doi:10.1016/j.displa.2023.102370 · Cited by 19

Secondary triage classification using an ensemble random forest technique

Azeez, D., Gan, K. B., Ali, M. A. M., & Ismail, M. S. (2015) · Technology and Health Care · doi:10.3233/thc-150907 · Cited by 17

An approach to text classification using dimensionality reduction and combination of classifiers

Jain, G., Ginwala, A., & Asiandogan, Y. A. (2005) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2004.1431521 · Cited by 17

Complexity of Hard-Core Set Proofs

Lu, C.-J., Tsai, S., & Wu, H.-L. (2011) · Computational Complexity · doi:10.1007/s00037-011-0003-7 · Cited by 17

Diagnosing the variance shifts signal in multivariate process control using ensemble classifiers

Cheng, C., & Lee, H.-T. (2015) · Journal of the Chinese Institute of Engineers · doi:10.1080/02533839.2015.1073662 · Cited by 17

Improved SMOTE algorithm for imbalanced dataset

Hengyu, Z. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9326603 · Cited by 17

Boosting Naı¨ve Bayes text classification using uncertainty-based selective sampling

Kim, H., Kim, J.-U., & Ra, Y.-G. (2005) · Neurocomputing · doi:10.1016/j.neucom.2004.09.003 · Cited by 17

Optimal Linear Combination of Facial Regions for Improving Identification Performance

Wong, K.-C., Lin, W.-Y., Hu, Y. H., Boston, N., & Zhang, X. (2007) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2007.895325 · Cited by 17

A comparative study of machine learning models for predicting the state of reactive mixing

Ahmmed, B., Mudunuru, M. K., Karra, S., James, S. C., & Vesselinov, V. V. (2021) · Journal of Computational Physics · doi:10.1016/j.jcp.2021.110147 · Cited by 17

Boosting with Diverse Base Classifiers

Dasgupta, S., & Long, P. M. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45167-9_21 · Cited by 17

Top-down decision tree learning as information based boosting

Takimoto, E., & Maruoka, A. (2003) · Theoretical Computer Science · doi:10.1016/s0304-3975(02)00181-0 · Cited by 17

Application of a new evolutionary programming/adaptive boosting hybrid to breast cancer diagnosis

Land, W. G., Masters, T. D., & Lo, J. Y.-C. (2002) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2000.870822 · Cited by 17

Majority gates vs. general weighted threshold gates

Goldmann, M., Håstad, J., & Razborov, A. A. (2003) · Structure in Complexity Theory Annual Conference · doi:10.1109/sct.1992.215375 · Cited by 17

Arabic named entity recognition.

Benajiba, Y. (2009) · doi:10.4995/thesis/10251/8318 · Cited by 17

Distribution-Independent Evolvability of Linear Threshold FunctionsPreprint

Feldman, V. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.4904 · Cited by 17

A boosting first-hitting-time model for survival analysis in high-dimensional settings

Bin, R. D., & Stikbakke, V. (2022) · Lifetime Data Analysis · doi:10.1007/s10985-022-09553-9 · Cited by 16

Application of tree-based methods in predicting the surface settlement arising from the tunnel excavation with large mix-shield

Huang, C., Du, H., Li, L., Ni, J., & Sun, Y. (2023) · SOILS AND FOUNDATIONS · doi:10.1016/j.sandf.2023.101379 · Cited by 16

A standardized comparative framework for machine learning techniques in lithium-ion battery state of health estimation

Pandit, R., & Ahlawat, N. (2025) · Future Batteries · doi:10.1016/j.fub.2025.100099 · Cited by 18

Probability estimation for multi-class classification using AdaBoost

Nie, Q., Jin, L., & Fei, S. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.06.008 · Cited by 16

Sampling from Non-smooth Distributions Through Langevin Diffusion

Luu, D. T., Fadili, J. M., & Chesneau, C. (2020) · Methodology And Computing In Applied Probability · doi:10.1007/s11009-020-09809-7 · Cited by 16

From algorithms to devices: Enabling machine learning through ultra-low-power VLSI mixed-signal array processing

Joshi, S., Kim, C., Ha, S., & Cauwenberghs, G. (2017) · Custom Integrated Circuits Conference · doi:10.1109/cicc.2017.7993650 · Cited by 16

On Adaptive Boosting for System Identification

Bjurgert, J., Valenzuela, P. E., & Rojas, C. R. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2754319 · Cited by 16

Honest compressions and their application to compression schemes

Livni, R., & Simon, P. (2013) · Journal of Machine Learning Research · Cited by 16

Comparing the Performance of Adaptive Boosted Classifiers in Anomaly based Intrusion Detection System for Networks

Sivanantham, S., Abirami, R., & Gowsalya, R. S. A. (2019) · International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN) · doi:10.1109/vitecon.2019.8899368 · Cited by 16

Identification of host-microbe interaction factors in the genomes of soft rot-associated pathogens Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14 with supervised machine learning

Ma, B., Charkowski, A. O., Glasner, J. D., & Perna, N. T. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-508 · Cited by 16

Managing Clinical Use of High-Alert Drugs: A Supervised Learning Approach to Pharmacokinetic Data Analysis

Hu, P. J., Cheng, T.-H., Wei, C., Yu, C.-H., Chan, A. L. F., & Wang, H. (2007) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2007.897700 · Cited by 16

Characterizing CO2 capture with aqueous solutions of LysK and the mixture of MAPA + DEEA using soft computing methods

Soleimani, R., Abooali, D., & Shoushtari, N. A. (2018) · Energy · doi:10.1016/j.energy.2018.09.061 · Cited by 16

An Ensemble of Deep Auto-Encoders for Healthcare Monitoring

Aouedi, O., Tobji, M. A. B., & Abraham, A. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-14347-3_10 · Cited by 16

Computing Optimal Hypotheses Efficiently for Boosting

Morishita, S. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45884-0_35 · Cited by 16

Consensus analysis of multiple classifiers using non-repetitive variables: Diagnostic application to microarray gene expression data

Su, Z., Hong, H., Perkins, R., Shao, X., Cai, W., & Tong, W. (2007) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2007.01.001 · Cited by 16

A Study of Composite or Hybrid Classifiers for Knowledge Discovery

Kumar, A., & Olmeda, I. (1999) · INFORMS journal on computing · doi:10.1287/ijoc.11.3.267 · Cited by 16

A Fourier Analysis Based Approach to Learning Decision Trees in a Distributed Environment

Park, B. H., Ayyagari, R., & Kargupta, H. (2001) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972719.19 · Cited by 16

Learning DNF Expressions from Fourier SpectrumPreprint

Feldman, V. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1203.0594 · Cited by 16

Learning from weakly dependent data under Dobrushin's conditionPreprint

Dagan, Y., Daskalakis, C., Dikkala, N., & Jayanti, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.09247 · Cited by 16

Classification Algorithm of Urban Point Cloud Data based on LightGBM

Shi, X., Cheng, Y., & Xue, D. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/631/5/052041 · Cited by 15

Statistical comparison of additive regression tree methods on ecological grassland data

Plant, E., King, R., & Kath, J. M. (2020) · Ecological Informatics · doi:10.1016/j.ecoinf.2020.101198 · Cited by 15

Estimating energy savings of ultra-high-performance fibre-reinforced concrete facade panels at the early design stage of buildings using gradient boosting machines

Abediniangerabi, B., Makhmalbaf, A., & Shahandashti, M. (2021) · Advances in Building Energy Research · doi:10.1080/17512549.2021.2011410 · Cited by 15

Evaluating and comparing bagging and boosting of hybrid learning for breast cancer screening

Zizaan, A., & Idri, A. (2023) · Scientific African · doi:10.1016/j.sciaf.2023.e01989 · Cited by 18

Predicting the climate impact of healthcare facilities using gradient boosting machines

Yin, H., Sharma, B., Hu, H., Liu, F., Kaur, M., Cohen, G., McConnell, R., & Eckel, S. P. (2023) · Cleaner Environmental Systems · doi:10.1016/j.cesys.2023.100155 · Cited by 15

An artificial intelligence approach to predict infants’ health status at birth

Harahap, T. H., Mansouri, S., Abdullah, O. S., Uinarni, H., Askar, S. K., Jabbar, T. L., Alawadi, A. H., & Hassan, A. Y. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105338 · Cited by 15

Improving Accuracy and Speed of Optimum-Path Forest Classifier Using Combination of Disjoint Training Subsets

Ponti, M. A., & Papa, J. P. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21557-5_26 · Cited by 15

A Multi-step-ahead CPU Load Prediction Approach in Distributed System

Yang, D., Cao, J., Yu, C., & Xiao, J. (2012) · International Conference on Cloud and Green Computing · doi:10.1109/cgc.2012.32 · Cited by 15

Specializing for predicting obesity and its co-morbidities

Goldstein, I., & Uzuner, Ö. (2008) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.11.001 · Cited by 15

Active classification using belief functions and information gain maximization

Reineking, T. (2015) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2015.12.005 · Cited by 15

Fashion Evaluation Method for Clothing Recommendation Based on Weak Appearance Feature

Zhang, Y., Liu, X., Shi, Y., Guo, Y., Xu, C., Zhang, E., Tang, S., & Fang, Z. (2017) · Scientific Programming · doi:10.1155/2017/8093057 · Cited by 15

Evaluation of machine learning methods for classification of rotational absorption spectra for gases in the 220–330 GHz range

Chowdhury, M. A. Z., Rice, T. E., & Oehlschlaeger, M. A. (2021) · Applied Physics B · doi:10.1007/s00340-021-07582-0 · Cited by 15

Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning

Wang, B., Tan, X., Guo, J., Xiao, T., Jiao, Y., Zhao, J., Wu, J., & Wang, Y. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14050943 · Cited by 15

NI-Raman spectroscopy combined with BP-Adaboost neural network for adulteration detection of soybean oil in camellia oil

Kuang, J., Luo, N., Hao, Z., Xu, J., He, X., & Shi, J. (2022) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-022-01430-1 · Cited by 15

Classifier Models in Intelligent CAPP Systems

Rojek, I. (2009) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-00563-3_32 · Cited by 15

Remembering Leo Breiman

Cutler, A. (2010) · The Annals of Applied Statistics · doi:10.1214/10-aoas427 · Cited by 15

Temperature Forecast Based on SVM Optimized by PSO Algorithm

Wang, G., Qiu, Y., & Li, H. (2010) · International Conference on Intelligent Computing and Cognitive Informatics (ICICCI) · doi:10.1109/icicci.2010.24 · Cited by 15

Learning Air-Data Parameters for Flush Air Data Sensing Systems

Srivastava, A., Meade, A. J., & Long, K. R. (2012) · Journal of Aerospace Computing Information and Communication · doi:10.2514/1.54947 · Cited by 15

An Analysis of Boosted Regression Trees to Predict the Strength Properties of Wood Composites

Carty, D. M. (2011) · Cited by 15

On boosting with polynomially bounded distributions

Bshouty, N. H., & Gavinsky, D. (2003) · Cited by 15

Training Hard-Margin Support Vector Machines Using Greedy Stagewise Algorithm

Bo, L., Wang, L., & Jiao, L. (2008) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2000576 · Cited by 15

An Improved Adaboost Algorithm Based on Uncertain Functions

Shu, X., & Wang, P. (2015) · International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII) · doi:10.1109/iciicii.2015.117 · Cited by 15

Spam Classification Using Adaptive Boosting Algorithm

Ali, A. B. M. S., & Xiang, Y. (2007) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis.2007.170 · Cited by 15

Combining Strategies for Extracting Relations from Text Collections

Agichtein, E., Eskin, E., & Gravano, L. (2000) · doi:10.7916/d8st821r · Cited by 15

Censored Data Regression in High‐Dimensional and Low‐Sample‐Size Settings for Genomic Applications

Li, H. (2007) · Wiley series in probability and statistics · doi:10.1002/9780470181218.ch22 · Cited by 15

Algorithms for enhancing pattern separability, feature selection and incremental learning with applications to gas sensing electronic nose systems

Robi Polikar, A. (2000) · doi:10.31274/rtd-180813-13978 · Cited by 15

Drifting games

Schapire, R. E. (1999) · Conference on Learning Theory · doi:10.1145/307400.307421 · Cited by 15

Compressing and Teaching for Low VC-Dimension

Moran, S., Shpilka, A., Wigderson, A., & Yehudayoff, A. (2015) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2015.12 · Cited by 14

A novel approach for predicting the concentration of exhaled aerosols exposure among healthcare workers in the operating room

Liu, Z., Huang, Z., Chu, J., Li, H., He, J., Lin, C., Jiang, C., Yao, G., & Fan, S. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110867 · Cited by 15

Lithofacies Identification from Wire-Line Logs Using an Unsupervised Data Clustering Algorithm

Hasan, M. M. U., Hasan, T., Shahidi, R., James, L. A., Peters, D. K., & Gosine, R. G. (2023) · Energies · doi:10.3390/en16248116 · Cited by 14

PnP-GA+: Plug-and-Play Domain Adaptation for Gaze Estimation Using Model Variants

Liu, R., Liu, Y., Wang, H., & Lu, F. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3348528 · Cited by 14

Monitoring of Low Chl-a Concentration in Hulun Lake Based on Fusion of Remote Sensing Satellite and Ground Observation Data

Zhang, S., Yinglan, A., Wang, L., Wang, Y., Zhang, X., Zhu, Y., & Ma, G. (2024) · Remote Sensing · doi:10.3390/rs16101811 · Cited by 14

Exploring the rheological and mechanical properties of alkali activated mortar incorporating waste foundry sand: A comprehensive experimental and machine learning investigation

Ashraf, M. M., Nazar, S., Iqbal, M., Yang, J., Ullah, R., & Hasan, M. U. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102973 · Cited by 15

Robust-GBDT: leveraging robust loss for noisy and imbalanced classification with GBDT

Luo, J., Quan, Y., & Xu, S. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02595-z · Cited by 14

An Experimental Evaluation of Boosting Methods for Classification

Stollhoff, R., Sauerbrei, W., & Schumacher, M. C. (2010) · Methods of Information in Medicine · doi:10.3414/me0543 · Cited by 14

ELM-MapReduce: MapReduce accelerated extreme learning machine for big spatial data analysis

Chen, J., Zheng, G., & Chen, H. (2013) · International Conference on Control and Automation · doi:10.1109/icca.2013.6565081 · Cited by 14

Dynamic weighting ensemble classifiers based on cross-validation

Zhu, Y., Ou, J., Chen, G., & Yu, H. (2010) · Neural Computing and Applications · doi:10.1007/s00521-010-0372-x · Cited by 14

Statistical Considerations in Combining Biomarkers for Disease Classification

Feng, Z., & Yasui, Y. (2004) · Disease Markers · doi:10.1155/2004/214152 · Cited by 14

Accelerating pattern-based time series classification: a linear time and space string mining approach

Raza, A., & Krämer, S. (2019) · Knowledge and Information Systems · doi:10.1007/s10115-019-01378-7 · Cited by 14

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

Elkashlan, M., Ahmad, R. M., Hajar, M., Jasmi, F. A., Corchado, J. M., Nasarudin, N. A., & Mohamad, M. S. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1182465 · Cited by 15

Guest Column

Viola, E. (2009) · ACM SIGACT News · doi:10.1145/1515698.1515709 · Cited by 14

Application of machine learning techniques for stock market prediction

Weng, B. (2017) · Cited by 14

Boosting conditional logit model

Shi, H., & Yin, G. (2017) · Journal of Choice Modelling · doi:10.1016/j.jocm.2017.07.002 · Cited by 14

Predication of Futures Market by Using Boosting Algorithm

Wu, Y., Mao, J.-M., & Li, W. (2018) · International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) · doi:10.1109/wispnet.2018.8538586 · Cited by 14

Rescaled Boosting in Classification

Wang, Y., Liao, X., & Lin, S.-B. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2885085 · Cited by 14

Research of Neural Network Classifier Based on FCM and PSO for Breast Cancer Classification

Zhang, L., Wang, L., Wang, X., Liu, K., & Abraham, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28942-2_58 · Cited by 14

Research and application of a hybrid system based on interpolation for forecasting direct economic losses of marine disasters

Zhao, X., Li, H., Ding, L., & Liu, M. (2019) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2019.101121 · Cited by 14

Distilled Camera-Aware Self Training for Semi-Supervised Person Re-Identification

Wu, A., Zheng, W., & Lai, J. (2019) · IEEE Access · doi:10.1109/access.2019.2950122 · Cited by 14

Implementation Strategy and Spatiotemporal Extensibility of Multipredictor Ensemble Model for Water Quality Parameter Retrieval With Multispectral Remote Sensing Data

Xu, M., Liu, H., Beck, R. A. D., Lekki, J., Yang, B., Liu, Y., Shu, S., Wang, S., Tokars, R. P., Anderson, R. C., et al. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3045921 · Cited by 14

Study of the Behavior of a New Boosting Algorithm for Recurrent Neural Networks

Assaad, M. A., Boné, R., & Cardot, H. (2005) · Lecture notes in computer science · doi:10.1007/11550907_28 · Cited by 14

AUTHENTICATION OF FUEL SPILL STANDARDS USING GAS CHROMATOGRAPHY/PATTERN RECOGNITION TECHNIQUES

Lavine, B. K., Vesanen, A., Brzozowski, D. M., & Mayfield, H. T. (2001) · Analytical Letters · doi:10.1081/al-100001579 · Cited by 14

The Ensemble Bridge Algorithm: A New Modeling Tool for Drug Discovery Problems

Culp, M. V., Johnson, K., & Michailidis, G. C. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci9003392 · Cited by 14

GA-Ensemble: a genetic algorithm for robust ensembles

Oh, D.-Y., & Gray, J. B. (2013) · Computational Statistics · doi:10.1007/s00180-013-0409-6 · Cited by 14

Selective SVMs Ensemble Driven by Immune Clonal Algorithm

Zhang, X., Wang, S., Shan, T., & Jiao, L. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-32003-6_33 · Cited by 14

Aggregating classifiers with ordinal response structure

Tutz, G., & Hechenbichler, K. (2005) · Journal of Statistical Computation and Simulation · doi:10.1080/00949650410001729481 · Cited by 14

Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural NetworksPreprint

Oono, K., & Suzuki, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.08550 · Cited by 14

Optimization and performance evaluation of machine learning classifiers for predicting construction quality and schedule

Fan, C. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106470 · Cited by 13

Deep learning model for fiber optic distributed acoustic sensing seismic data compression based on autoencoder and recurrent neural networks

Wang, H., Yang, X., Zeng, S., Liu, T., Wang, X., & Wang, Y. (2024) · Applied Optics · doi:10.1364/ao.527625 · Cited by 13

Unveiling desert region in inert doublet model assisted by Peccei-Quinn symmetry

Ghosh, A., & Konar, P. (2024) · Journal of High Energy Physics · doi:10.1007/jhep09(2024)104 · Cited by 13

A time-series modeling method based on the boosting gradient-descent theory

Gao, Y., Pan, J., Ji, G., & Gao, F. (2011) · Science China Technological Sciences · doi:10.1007/s11431-011-4340-1 · Cited by 13

An intrusion detection method based on decision tree

Liu, Y., Li, N., Shi, L., & Li, F. (2010) · International Conference on E-Health Networking Digital Ecosystems and Technologies · doi:10.1109/edt.2010.5496597 · Cited by 13

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