Locally Weighted Learning for ARMA Time Series.(Dept.M)

Real-Time Robot Learning with Locally Weighted Statistical Learning

Computer Science and Neuroscience, HNB-103, Univ. of Southern California, Los Angeles, CA 90089-2520 *College of Computing, Georgia Institute of Technology, 801 Atlantic Drive, Atlanta, GA 30332-0280 ⊕Kawato Dynamic Brain Project (ERATO/JST), 2-2 Hikaridai, Seika-cho, Soraku-gun, 619-02 Kyoto, Japan ⊕Laboratory for Information Synthesis, Riken Brain Science Research Institute, Wako, Saitama, Japan

Locally Weighted Learning

Locally Weighted Learning is a class of function approximation techniques, where a prediction is done by using an approximated local model around the current point of interest. This paper gives an general overview on the topic and shows two different solution algorithms. Finally some successful applications of LWL in the field of Robot Learning are presented.

Locally Weighted Learning for Naive Bayes Classifier

As a consequence of the strong and usually violated conditional independence assumption (CIA) of naive Bayes (NB) classifier, the performance of NB becomes less and less favorable compared to sophisticated classifiers when the sample size increases. We learn from this phenomenon that when the size of the training data is large, we should either relax the assumption or apply NB to a “reduced” da...

Local dimensionality reduction for locally weighted learning

Incremental learning of sensorimotor transformations in high dimensional spaces is one of the basic prerequisites for the success of autonomous robot devices as well as biological movement systems. So far, due to sparsity of data in high dimensional spaces, learning in such settings requires a signi cant amount of prior knowledge about the learning task, usually provided by a human expert. In t...

A Locally Weighted Learning Tutorial using Vizier