Is Nonparametric Learning Practical in Very High Dimensional Spaces?

Many of the challenges faced by the £eld of Computational Intelligence in building intelligent agents, involve determining mappings between numerous and varied sensor inputs and complex and ¤exible 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 £rst on 10 well known regression data sets, where it ...

• Type: Conference
• Year: 1997
• Where: IJCAI
• Authors: Gregory Z. Grudic, Peter D. Lawrence
• Keywords: IJCAI 1997, IJCAI 2007, Nonparametric Learning, Nonparametric Learning Algorithm, Nonparametric Learning Techniques