Communications of the ACM
Mechanisms of implicit learning: connectionist models of sequence processing
Mechanisms of implicit learning: connectionist models of sequence processing
Machine Learning
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
Multi-agent reinforcement learning: weighting and partitioning
Neural Networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Machine Learning
Machine Learning
Bidirectional Dynamics for Protein Secondary Structure Prediction
Sequence Learning - Paradigms, Algorithms, and Applications
Classifier fitness based on accuracy
Evolutionary Computation
Adaptive mixtures of local experts
Neural Computation
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Engineering Self-modeling Systems: Application to Biology
Engineering Societies in the Agents World IX
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In this paper, we illustrate a system aimed at predicting protein secondary structures. Our proposal falls in the category of multiple experts, a machine learning technique that –under the assumption of absent or negative correlation in experts' errors– may outperform monolithic classifier systems. The prediction activity results from the interaction of a population of experts, each integrating genetic and neural technologies. Roughly speaking, an expert of this kind embodies a genetic classifier designed to control the activation of a feedforward artificial neural network. Genetic and neural components (i.e., guard and embedded predictor, respectively) are devoted to perform different tasks and are supplied with different information: Each guard is aimed at (soft-) partitioning the input space, insomuch assuring both the diversity and the specialization of the corresponding embedded predictor, which in turn is devoted to perform the actual prediction. Guards deal with inputs that encode information strictly related with relevant domain knowledge, whereas embedded predictors process other relevant inputs, each consisting of a limited window of residues. To investigate the performance of the proposed approach, a system has been implemented and tested on the RS126 set of proteins. Experimental results point to the potential of the approach.