Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Statistical Analysis: A Computer Oriented Approach
Statistical Analysis: A Computer Oriented Approach
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Evolving Output Codes for Multiclass Problems
IEEE Transactions on Evolutionary Computation
A Bayesian Network Approach to Program Generation
IEEE Transactions on Evolutionary Computation
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
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The pattern recognition applications like speech recognition, text classification and image recognition result in the solution of multi-class problems. Multi-class problems are reduced into several two class problems using the Machine Learning techniques such as Neural Networks and Support Vector Machines. We propose a hybrid approach for the design of output codes for multi-class pattern recognition problems. This approach has the advantage of taking into account the different aspects that are relevant for a code matrix to achieve good performance. Conventionally, code matrix is designed based on either the features of the problem or the features of the code matrix. The proposed work, focused on designing a new code matrix based on both the features of the problem and code matrix. This model aims at developing a hybrid version of ECOC and adaptive Recursive ECOC with BBO to achieve maximum classification accuracy and minimum computational time. Validation of the results has been performed using non-parametric statistical tests. The statistical results demonstrate that the evolving output codes through BBO provide a general-purpose method for improving the performance of base learners for real world multi-class pattern recognition problems.