Neural network learning and expert systems
Neural network learning and expert systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosted mixture of experts: an ensemble learning scheme
Neural Computation
Neural-Symbolic Learning System: Foundations and Applications
Neural-Symbolic Learning System: Foundations and Applications
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Computational Intelligence in Economics and Finance: Volume II
Computational Intelligence in Economics and Finance: Volume II
Adaptive mixtures of local experts
Neural Computation
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The last several years have seen an increasing emphasis on mathematical models, both based on statistics and on machine-learning. Today Bayesian nets, neural nets, support vector machines (SVM), and induction trees, are commonly used in the analysis of scientific data. Moreover, a recent emphasis in the modelling community is on improving the performance of classifiers through ensembling more different and accurate models in order to reduce the prediction error. Ensembling in fact is a way of taking advantage of good models that make errors in different parts of the data space. We will outline the developments in model construction and evaluation through those techniques justify their use and propose some quantitative structure activity relationships (QSAR) and models based on ensembling. The models presented here are in the area of predicting acute toxicity for the purpose of regulatory systems. The emphasis is on the better performances of ensembles, since the general goal of delivering usable QSAR models requires others that are out of the scope of this article.