Elements of information theory
Elements of information theory
The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Training products of experts by minimizing contrastive divergence
Neural Computation
A New Learning Algorithm for Mean Field Boltzmann Machines
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Information geometry of U-Boost and Bregman divergence
Neural Computation
Multiclass Boosting for Weak Classifiers
The Journal of Machine Learning Research
Robustifying AdaBoost by Adding the Naive Error Rate
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Good error-correcting codes based on very sparse matrices
IEEE Transactions on Information Theory
Information geometry of turbo and low-density parity-check codes
IEEE Transactions on Information Theory
A unified framework of binary classifiers ensemble for multi-class classification
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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In this letter, we present new methods of multiclass classification that combine multiple binary classifiers. Misclassification of each binary classifier is formulated as a bit inversion error with probabilistic models by making an analogy to the context of information transmission theory. Dependence between binary classifiers is incorporated into our model, which makes a decoder a type of Boltzmann machine. We performed experimental studies using a synthetic data set, data sets from the UCI repository, and bioinformatics data sets, and the results show that the proposed methods are superior to the existing multiclass classification methods.