C4.5: programs for machine learning
C4.5: programs for machine learning
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
Nonparametric classification with polynomial MPMC cascades
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A non-contact device for tracking gaze in a human computer interface
Computer Vision and Image Understanding - Special issue on eye detection and tracking
A non-contact device for tracking gaze in a human computer interface
Computer Vision and Image Understanding - Special issue on eye detection and tracking
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Many of the challenges faced by the field of Computational Intelligence in building intelligent agents, involve determining mappings between numerous and varied sensor inputs and complex and flexible action sequences. In applying nonparametric learning techniques to such problems we must therefore ask: "Is nonparametric learning practical in very high dimensional spaces?" Contemporary wisdom states that variable selection and a "greedy" choice of appropriate functional structures are essential ingredients for nonparametric learning algorithms. However, neither of these strategies is practical when learning problems have thousands of input variables, and tens of thousands of learning examples. We conclude that such nonparametric learning is practical by using a methodology which does not use either of these techniques. We propose a simple nonparametric learning algorithm to support our conclusion. The algorithm is evaluated first on 10 well known regression data sets, where it is shown to produce regression functions which are as good or better than published results on 9 of these data sets. The algorithm is further evaluated on 15 large, very high dimensional data sets (40,000 learning examples of 100, 200, 400, 800 and 1600 dimensional data) and is shown to construct effective regression functions despite the presence of noise in both inputs and outputs.