Unified decision combination framework

[1]  Bhagavatula Vijaya Kumar,et al.  Combining neural networks using the ranking figure of merit , 1996, Defense + Commercial Sensing.

[2]  Raúl Rojas,et al.  A Short Proof of the Posterior Probability Property of Classifier Neural Networks , 1996, Neural Computation.

[3]  Bhagavatula Vijaya Kumar,et al.  Learning ranks with neural networks , 1995, SPIE Defense + Commercial Sensing.

[4]  Sung-Bae Cho,et al.  Combining multiple neural networks by fuzzy integral for robust classification , 1995, IEEE Trans. Syst. Man Cybern..

[5]  Jonathan J. Hull,et al.  A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Roberto Battiti,et al.  Democracy in neural nets: Voting schemes for classification , 1994, Neural Networks.

[7]  Bhagavatula Vijaya Kumar,et al.  Efficient autonomous learning for statistical pattern recognition , 1994, Defense, Security, and Sensing.

[8]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Bhagavatula Vijaya Kumar,et al.  Differential theory of learning for efficient neural network pattern recognition , 1993, Defense, Security, and Sensing.

[10]  Padhraic Smyth,et al.  On loss functions which minimize to conditional expected values and posterior proba- bilities , 1993, IEEE Trans. Inf. Theory.

[11]  Ii John Benjamin Hampshire.  A differential theory of learning for efficient statistical pattern recognition , 1993 .

[12]  Jon Atli Benediktsson,et al.  Consensus theoretic classification methods , 1992, IEEE Trans. Syst. Man Cybern..

[13]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[14]  T. Ho.  A theory of multiple classifier systems and its application to visual word recognition , 1992 .

[15]  Ganesh Mani.  Lowering Variance of Decisions by Using Artificial Neural Network Portfolios , 1991, Neural Computation.

[16]  Jon Atli Benediktsson,et al.  A Consensual Neural Network , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

[17]  Richard Lippmann,et al.  Neural Network Classifiers Estimate Bayesian a posteriori Probabilities , 1991, Neural Computation.

[18]  Barak A. Pearlmutter,et al.  Equivalence Proofs for Multi-Layer Perceptron Classifiers and the Bayesian Discriminant Function , 1991 .

[19]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Amro El-Jaroudi,et al.  A new error criterion for posterior probability estimation with neural nets , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[21]  Sargur N. Srihari,et al.  Combination of Structural Classifiers , 1990 .

[22]  Y. Le Cun,et al.  Comparing different neural network architectures for classifying handwritten digits , 1989, International 1989 Joint Conference on Neural Networks.

[23]  Spyros Makridakis,et al.  Why combining works , 1989 .

[24]  R. Clemen.  Combining forecasts: A review and annotated bibliography , 1989 .

[25]  Christian Genest,et al.  Combining Probability Distributions: A Critique and an Annotated Bibliography , 1986 .

[26]  R. L. Winkler,et al.  Averages of Forecasts: Some Empirical Results , 1983 .

[27]  R. Bordley.  A Multiplicative Formula for Aggregating Probability Assessments , 1982 .

[28]  Sargur N. Srihari.  Reliability analysis of majority vote systems , 1982, Inf. Sci..

[29]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[30]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .