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Incremental Online Learning in High Dimensions

By Sethu Vijayakumar, Aaron D'Souza and Stefan Schaal


Locally weighted projection regression (LWPR) is a new algorithm for incremental non-linear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its cor

Topics: Online learning
Publisher: MIT Press
Year: 2010
OAI identifier: oai:www.era.lib.ed.ac.uk:1842/3698

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  1. (2000). A global geometric framework for nonlinear dimensionality reduction. doi
  2. (1993). A statistical view of some chemometric tools. doi
  3. (1998). A tutorial on support vector regression
  4. (1999). Advances in kernel methods: Support vector learning.
  5. (2003). Aircraft control and simulation.
  6. (1995). An alternative model for mixtures of experts. In
  7. (1984). An introduction to latent variable models. doi
  8. (2001). Are internal models of the entire body learnable?
  9. (1995). Bayesian data analysis. doi
  10. (1990). Classical and modern regression with applications. doi
  11. (2004). Composite adaptive control with locally weighted statistical learning. doi
  12. (1998). Constructive incremental learning from only local information. doi
  13. (1982). EM algorithms for ML factor analysis. doi
  14. (1996). Gaussian processes for regression.
  15. (1990). Generalized additive models. doi
  16. (1998). Local adaptive subspace regression.
  17. (1998). Local dimensionality reduction. In doi
  18. (1993). Local regression: Automatic kernel carpentry. doi
  19. (1997). Locally weighted learning. doi
  20. (1992). Multivariate density estimation. doi
  21. (2000). New support vector algorithms. doi
  22. (2000). Nonlinear dimensionality reduction by local linear embedding. doi
  23. (1989). Numerical recipes in C: The art of scientific computing. Cambridge: doi
  24. (1975). Perspectives in probability and statistics.
  25. (1999). Probabilistic principal component analysis. doi
  26. (1981). Projection pursuit regression. doi
  27. (1997). Receptive field weighted regression (Tech.
  28. (1980). Regression diagnostics. doi
  29. (1999). RKHS based functional analysis for exact incremental learning. doi
  30. (2000). SMEM algorithm for mixture models. doi
  31. (2002). Supervised dimensionality reduction of intrinsically low-dimensional data. doi
  32. Support vector machine—reference manual (Tech.
  33. (1990). The cascade-correlation learning architecture.
  34. (1999). The UCI KDD archive. London:
  35. (1986). Theory and practice of recursive identification. doi
  36. (2000). Variational inference for Bayesian mixtures of factoranalysers.InS.A.Solla,T.K.Leen,&K.-R.M¨ uller(Eds.),Advancesinneural information processing systems,

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