Communications of the ACM
Original Contribution: Stacked generalization
Neural Networks
Data fusion in robotics and machine intelligence
Data fusion in robotics and machine intelligence
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Agnostic PAC learning of functions on analog neural nets
Neural Computation
Machine Learning
Machine Learning
Error reduction through learning multiple descriptions
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Averaging regularized estimators
Neural Computation
Optimal linear combinations of neural networks
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Function Estimation by Feedforward Sigmoidal Networks with BoundedWeights
Neural Processing Letters
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new algorithm for learning in piecewise-linear neural networks
Neural Networks
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Theoretical Advances in Neural Computation and Learning
Theoretical Advances in Neural Computation and Learning
A Principal Components Approach to Combining Regression Estimates
Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
The lack of a priori distinctions between learning algorithms
Neural Computation
Nonparametric estimation via empirical risk minimization
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Learning algorithms for feedforward networks based on finite samples
IEEE Transactions on Neural Networks
Information Fusion Methods Based on Physical Laws
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier Fusion Using Shared Sampling Distribution for Boosting
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Pattern recognition with a Bayesian kernel combination machine
Pattern Recognition Letters
Mining extremely small data sets with application to software reuse
Software—Practice & Experience
Design and implementation of a robust sensor data fusion system for unknown signals
DCOSS'10 Proceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems
ACM Transactions on Embedded Computing Systems (TECS)
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In a multiple sensor system, sensor $S_i$, $i=1, 2 \ldots , N$, outputs $Y^{(i)}\in [0,1]$, according to an unknown probability distribution $P_{Y^{(i)} | X }$, in response to input $X \in [0,1]$. We choose a fuser驴that combines the outputs of sensors驴from a function class ${\cal{F}} = \{ f : [0,1]^N \mapsto [0,1] \}$ by minimizing empirical error based on an iid sample. If $\cal{F}$ satisfies the isolation property, we show that the fuser performs at least as well as the best sensor in a probably approximately correct sense. Several well-known fusers, such as linear combinations, special potential functions, and certain feedforward networks, satisfy the isolation property.