Prediction games and arcing algorithms
Neural Computation
PAC Analogues of Perceptron and Winnow Via Boosting the Margin
Machine Learning
StrCombo: combination of string recognizers
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Automatic Design of Multiple Classifier Systems by Unsupervised Learning
MLDM '99 Proceedings of the First International Workshop on Machine Learning and Data Mining in Pattern Recognition
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Multiple Classifier Combination Methodologies for Different Output Levels
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Complexity of Data Subsets Generated by the Random Subspace Method: An Experimental Investigation
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Information Analysis of Multiple Classifier Fusion
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Limiting the Number of Trees in Random Forests
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Classifier Combinations: Implementations and Theoretical Issues
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Local voting of weak classifiers
International Journal of Knowledge-based and Intelligent Engineering Systems
Credit risk analysis using a hybrid data mining model
International Journal of Intelligent Systems Technologies and Applications
Selective costing voting for bankruptcy prediction
International Journal of Knowledge-based and Intelligent Engineering Systems
Increasing soft classification accuracy through the use of an ensemble of classifiers
International Journal of Remote Sensing
A PSO Based Adaboost Approach to Object Detection
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
POLYBIO: Multimodal Biometric Data Acquisition Platform and Security System
Biometrics and Identity Management
Committee machines for facial-gender recognition
International Journal of Hybrid Intelligent Systems
Bagging different instead of similar models for regression and classification problems
International Journal of Computer Applications in Technology
Combination methodologies of multi-agent hyper surface classifiers: design and implementation issues
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
A novel training weighted ensemble (TWE) with application to face recognition
Applied Soft Computing
Intrusion detection using neural based hybrid classification methods
Computer Networks: The International Journal of Computer and Telecommunications Networking
Variable randomness in decision tree ensembles
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Markov chains pattern recognition approach applied to the medical diagnosis tasks
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
An attention-based decision fusion scheme for multimedia information retrieval
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Dynamic fusion method using Localized Generalization Error Model
Information Sciences: an International Journal
Model combination for support vector regression via regularization path
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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To obtain classification systems with both good generalization performance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers, where weak classifiers are linear classifiers (perceptrons) which can do a little better than making random guesses. A randomized algorithm is proposed to find the weak classifiers. They are then combined through a majority vote. As demonstrated through systematic experiments, the method developed is able to obtain combinations of weak classifiers with good generalization performance and a fast training time on a variety of test problems and real applications. Theoretical analysis on one of the test problems investigated in our experiments provides insights on when and why the proposed method works. In particular, when the strength of weak classifiers is properly chosen, combinations of weak classifiers can achieve a good generalization performance with polynomial space- and time-complexity