Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploration in active learning
The handbook of brain theory and neural networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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Pattern Recognition Letters
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
Choosing where to look next in a mutation sequence space
Bioinformatics
Hierarchical sampling for active learning
Proceedings of the 25th international conference on Machine learning
Active learning with direct query construction
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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IEEE Transactions on Evolutionary Computation
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Curiosity: From psychology to computation
ACM Computing Surveys (CSUR)
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This paper presented a novel active linear discriminant analysis (LDA) learning method in the form of curiositydriven incremental LDA (cILDA) and multiple cILDA agents cooperative learning (mcILDA). The curiosity in psychology here is modelled mathematically as a discriminability residue inbetween instance space and its corresponding eigenspace. As the learning proceeds, the curiosity of an individual agent updates over time by two incremental learning processes: One updates the characterization of eigenspace and another re-calculates the curiosity. In the multi-agent scenario, individual agent communicates and cooperates with each other at every learning stage to discover the discriminant characterization of the whole pattern. In the experiment, we described how the discriminative instances could be significantly selected based on the curiosity with, at most, minor sacrifices in learning rate and classification accuracy. The experimental results show that the proposed curiosity learning performs gracefully under different level of redundancy, and the proposed cILDA/mcILDA learning system is capable of learning less instances, but has more often an improved discrimination performance.