[CONF] Apache Lucene Mahout: Locally Weighted Linear Regression (page created)

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Locally Weighted Linear Regression (MAHOUT) created by Isabel Drost
   http://cwiki.apache.org/confluence/display/MAHOUT/Locally+Weighted+Linear+Regression

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h1. Locally Weighted Linear Regression

Model-based methods, such as SVM, Naive Bayes and the mixture of Gaussians, use the data to build a parameterized model. After training, the model is used for predictions and the data are generally discarded. In contrast, "memory-based" methods are non-parametric approaches that explicitly retain the training data, and use it each time a prediction needs to be made. Locally weighted regression (LWR) is a memory-based method that performs a regression around a point of interest using only training data that are "local" to that point. Source: http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/cohn96a-html/node7.html

h2. Strategy for parallel regression

h2. Design of packages

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