A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting

1997/08/01 by Yoav Freund, Robert E Schapire, Robert E. Schapire · 20,469 citations
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Algorithm #Artificial intelligence #Auction Theory and Applications #Boosting (machine learning) #Bounded function #Computer science #Decision rule #Generalization #Machine Learning and Algorithms #Machine learning #Mathematical optimization #Mathematics #Multiplicative function

paper · doi:10.1006/jcss.1997.1504

published in Journal of Computer and System Sciences 55(1), 119-139 (Elsevier BV)

openalex publication_date 1997/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

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• Linear Programming in the Semi-streaming Model with Application to the Maximum Matching Problem
• Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning
• Hierarchical Feature-Aware Tracking
• Comprehensive geoneutrino analysis with Borexino
• Data Quality Matters For Adversarial Training: An Empirical Study
• Learning with Feature Evolvable Streams
• Beating the Multiplicative Weights Update Algorithm
• Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models
• Predictive No-Reference Assessment of Video Quality
• Efficient Learning of Ensembles with QuadBoost
• Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
• Search in Imperfect Information Games
• Improved Quantum Boosting
• Efficiently Inducing Features of Conditional Random Fields
• A new Hedging algorithm and its application to inferring latent random variables
• Machine Truth Serum
• RandomBoost: Simplified Multi-class Boosting through Randomization
• XtracTree: a Simple and Effective Method for Regulator Validation of Bagging Methods Used in Retail Banking
• Optimistic and Adaptive Lagrangian Hedging
• Connecting Interpretability and Robustness in Decision Trees through Separation
• Reliable generation of privacy-preserving synthetic electronic health record time series via diffusion models
• A review on ranking problems in statistical learning
• Data Mining in Healthcare and Biomedicine: A Survey of the Literature
• No-Regret and Incentive-Compatible Online Learning
• Characterizing the Sample Complexity of Private Learners
• Machine Learning Approach for Rock Mass Classification with Imbalanced Database of TBM Tunnelling in Himalayan Geology
• Benchmarking the performance and uncertainty of machine learning models in estimating scour depth at sluice outlets
• A novel AI-powered method for robust identification of operational phases in refrigerators
• Machine learning-guided discovery and design of non-hemolytic peptides
• Stochastic Multiplicative Weights Updates in Zero-Sum Games
• Oblique Decision Trees from Derivatives of ReLU Networks
• Boost Neural Networks by Checkpoints
• Selective Cascade of Residual ExtraTrees
• No Spare Parts: Sharing Part Detectors for Image Categorization
• Autism-related dietary preferences mediate autism-gut microbiome associations
• Visual Transfer Learning: Informal Introduction and Literature Overview
• Evaluating Road Crash Severity Prediction with Balanced Ensemble Models
• Exclusivity Regularized Machine
• Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits
• Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions
• Artificial Prediction Markets for Online Prediction of Continuous Variables-A Preliminary Report
• Structure-aware machine learning strategies for antimicrobial peptide discovery
• Detection and classification of landmines using machine learning applied to metal detector data
• Partitioning Large Scale Deep Belief Networks Using Dropout
• Towards More Efficient and Effective Inference: The Joint Decision of Multi-Participants
• RoNGBa: A Robustly Optimized Natural Gradient Boosting Training Approach with Leaf Number Clipping
• AdaBoost-assisted Extreme Learning Machine for Efficient Online Sequential Classification
• Dive into Decision Trees and Forests: A Theoretical Demonstration
• Risk and parameter convergence of logistic regression
• Automating Predictive Modeling Process using Reinforcement Learning
• Adaptive Random SubSpace Learning (RSSL) Algorithm for Prediction
• Characterizing the implicit bias via a primal-dual analysis
• Adaptive Channel Allocation Spectrum Etiquette for Cognitive Radio Networks
• Convex Polytope Trees
• Face Detection Using Adaboosted SVM-Based Component Classifier
• Statistical Consistency of Finite-dimensional Unregularized Linear Classification
• Learning from Imbalanced Data
• Not So Naive Bayes: Aggregating One-Dependence Estimators
• Optimal rates of aggregation in classification under low noise assumption
• Cost-minimising strategies for data labelling : optimal stopping and active learning
• Towards a combinatorial characterization of bounded memory learning
• Least angle and ℓ1 penalized regression: A review
• Adaptive Task Sampling for Meta-Learning
• Using a VOM model for reconstructing potential coding regions in EST sequences
• New multicategory boosting algorithms based on multicategory Fisher-consistent losses
• Boosting Algorithms: Regularization, Prediction and Model Fitting
• On preserving non-discrimination when combining expert advice
• MOAI: A methodology for evaluating the impact of indoor airflow in the transmission of COVID-19
• Grid Binary LOgistic REgression (GLORE): building shared models without sharing data
• Online Convex Optimization in Changing Environments and its Application to Resource Allocation
• Semantically Consistent Regularization for Zero-Shot Recognition
• Interpretable Companions for Black-Box Models
• Majority Voting and the Condorcet's Jury Theorem
• Efficient, Noise-Tolerant, and Private Learning via Boosting
• Observation of the B s 0 → J/ψϕϕ decay
• An ensemble approach to improved prediction from multitype data
• A Novel Family of Boosted Online Regression Algorithms with Strong Theoretical Bounds
• Generalization bounds for averaged classifiers
• Top 10 algorithms in data mining
• Rapid object detection using a boosted cascade of simple features
• Learning to Predict Combinatorial Structures
• Observation of new Ξ_c⁰ baryons decaying to Λ_c⁺ K^-
• Visual saliency estimation by integrating features using multiple kernel learning
• Tweedie Gradient Boosting for Extremely Unbalanced Zero-inflated Data
• Automatic detection of fiducials landmarks toward development of an application for EEG electrodes location (digitization): Occipital structured sensor based-work
• Rule Covering for Interpretation and Boosting
• Multi-Modal Music Information Retrieval: Augmenting Audio-Analysis with Visual Computing for Improved Music Video Analysis
• Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost
• Online Coordinate Boosting
• Adaptive and Efficient Algorithms for Tracking the Best Expert
• Search for the rare decay K_S⁰→ μ⁺μ^-
• A Bayesian Network Classifier that Combines a Finite Mixture Model and a Naive Bayes Model
• Boosting in the Presence of Outliers: Adaptive Classification With Nonconvex Loss Functions
• Observation of the suppressed decay Λ b 0 → pπ − μ + μ −
• An Online Learning-based Framework for Tracking
• Observation of the decay Λ b 0 → pK − μ + μ − and a search for CP violation
• AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
• Data-driven Soft Sensors in the process industry
• Pairwise Boosted Audio Fingerprint
• A Survey of Applications and Human Motion Recognition with Microsoft Kinect
• A Principal Components Approach to Combining Regression Estimates
• What is Machine Learning? A Primer for the Epidemiologist
• Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector
• Precision Measurement of the Mass and Lifetime of theΞb−Baryon
• Measurement of theCPAsymmetry inB+→K+μ+μ−Decays
• Permafrost Distribution in the Southern Carpathians, Romania, Derived From Machine Learning Modeling
• On Optimal Robustness to Adversarial Corruption in Online Decision Problems
• Measurement of theBs0→μ+μ−Branching Fraction and Search forB0→μ+μ−Decays at the LHCb Experiment
• Eye state recognition based on deep integrated neural network and transfer learning
• Classifier Ensembles for Changing Environments
• Incremental Learning of Concept Drift in Nonstationary Environments
• ToPs: Ensemble Learning With Trees of Predictors
• Augmented Outcome-weighted Learning for Optimal Treatment Regimes
• Classifier ensemble creation via false labelling
• Ensemble regression framework for accurate thrust prediction in UAV gas turbine propulsion systems
• B2BGAN: A Backbone-to-Branches GAN-Based Oversampling Approach for Class-Imbalanced Tabular Data
• Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data
• Efficient Constrained Regret Minimization
• Search for the B s 0 → η′ϕ decay
• Malicious web domain identification using online credibility and performance data by considering the class imbalance issue
• Higgs boson potential at colliders: Status and perspectives
• First study of the CP-violating phase and decay-width difference in Bs0→ψ(2S)ϕ decays
• When Does Diversity Help Generalization in Classification Ensembles?
• BART: Bayesian additive regression trees
• Enhancing Certifiable Robustness via a Deep Model Ensemble
• Measurement of the lifetimes of promptly produced Ω⁰_c and Ξ⁰_c baryons
• Region Based Ensemble Learning Network for Fine-Grained Classification
• k-experts -- Online Policies and Fundamental Limits
• Artist, Style and Year Classification using Face Recognition and Clustering with Convolutional Neural Networks
• WIQA: A dataset for "What if..." reasoning over procedural text
• META-DES.Oracle: Meta-learning and feature selection for dynamic ensemble selection
• Additive Gaussian Process Regression
• Bandits with Switching Costs: T^2/3 Regret
• The IceCube realtime alert system
• Search for a W′ boson decaying to a bottom quark and a top quark in pp collisions at s=7 TeV
• Predicting Movie Genres Based on Plot Summaries
• Feature Selection for Huge Data via Minipatch Learning
• A Bayesian Boosting Model
• Contrast trees and distribution boosting
• Combined Learning of Salient Local Descriptors and Distance Metrics for Image Set Face Verification
• Angular analysis and differential branching fraction of the decay B s 0 → ϕμ + μ −
• ANGLE: A SEQUENCING ERRORS RESISTANT PROGRAM FOR PREDICTING PROTEIN CODING REGIONS IN UNFINISHED cDNA
• Measurement of CP violation in B0 → D∓π± decays
• Statistical classification techniques for photometric supernova typing
• Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines
• META-DES: A dynamic ensemble selection framework using meta-learning
• FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection
• Supervised Metric Learning with Generalization Guarantees
• MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling
• Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes
• Impact of salinity on anaerobic ceramic membrane bioreactor for textile wastewater treatment: Process performance, membrane fouling and machine learning models
• Stochastic Particle Gradient Descent for Infinite Ensembles
• Anomaly detection for machine learning redshifts applied to SDSS galaxies
• Stacking machine learning classifiers to identify Higgs bosons at the LHC
• How boosting the margin can also boost classifier complexity
• A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images
• Risk bounds for CART classifiers under a margin condition
• SEARCH FOR PROMPT NEUTRINO EMISSION FROM GAMMA-RAY BURSTS WITH ICECUBE
• Superhuman AI for heads-up no-limit poker: Libratus beats top professionals
• Ensemble Classifier Approach in Breast Cancer Detection and Malignancy Grading - A Review
• Identifying metabolic enzymes with multiple types of association evidence. [europepmc]
• Overview of BioCreative II gene normalization. [europepmc]
• A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia. [europepmc]
• SHIFTX2: significantly improved protein chemical shift prediction. [europepmc]
• Harnessing context sensing to develop a mobile intervention for depression. [europepmc]
• Multi-atlas segmentation with joint label fusion and corrective learning-an open source implementation. [europepmc]
• Aggregation risk prediction for antibodies and its application to biotherapeutic development. [europepmc]
• Discrimination of cell cycle phases in PCNA-immunolabeled cells. [europepmc]
• Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study. [europepmc]
• Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data. [europepmc]
• Population Genomic Analysis of 1,777 Extended-Spectrum Beta-Lactamase-Producing Klebsiella pneumoniae Isolates, Houston, Texas: Unexpected Abundance of Clonal Group 307. [europepmc]
• Machine learning for epigenetics and future medical applications. [europepmc]
• In silico prediction of novel therapeutic targets using gene-disease association data. [europepmc]
• Salt-responsive gut commensal modulates T H 17 axis and disease. [europepmc]
• Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. [europepmc]
• Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia. [europepmc]
• Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study. [europepmc]
• q2-sample-classifier: machine-learning tools for microbiome classification and regression. [europepmc]
• Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes. [europepmc]
• Early Detection of Alzheimer's Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning. [europepmc]
• Big-Data Science in Porous Materials: Materials Genomics and Machine Learning. [europepmc]
• Machine learning-guided discovery and design of non-hemolytic peptides. [europepmc]
• Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review. [europepmc]
• The benefits and pitfalls of machine learning for biomarker discovery. [europepmc]
• Large language models streamline automated machine learning for clinical studies. [europepmc]