Current Slide

Small screen detected. You are viewing the mobile version of SlideWiki. If you wish to edit slides you will need to use a larger device.

  • Linear regression: Y = w X + b
    • Two regression coefficients, w and b, specify the line and are to be estimated by using the data at hand
    • Using the least squares criterion to the known values of Y1, Y2, …, X1, X2, ….
  • Multiple regression: Y = b + b1 X1 + b2 X2
    • Many nonlinear functions can be transformed into the above
  • Log-linear models:
    • Approximate discrete multidimensional probability distributions
    • Estimate the probability of each point (tuple) in a multi-dimensional space for a set of discretized attributes, based on a smaller subset of dimensional combinations
    • Useful for dimensionality reduction and data smoothing

Regress Analysis and Log-Linear Models


Speaker notes:

Content Tools

Sources

There are currently no sources for this slide.