| Package | Description |
|---|---|
| org.openimaj.ml.regression | |
| org.openimaj.ml.timeseries.aggregator | |
| org.openimaj.ml.timeseries.processor |
| Class and Description |
|---|
| LinearRegression
Given a set of independant variables a linear regressions finds the optimal
vector B such that: (Y - Xb)^2 = 0 (Y - Xb)^{T}(Y-Xb) = 0
calculated by assuming a convex shape of (Y - Xb) with varying values of b
(reasonable as the function is linear) and then calculating the point at
which the first derivative of this function is 0.
|
| Class and Description |
|---|
| LinearRegression
Given a set of independant variables a linear regressions finds the optimal
vector B such that: (Y - Xb)^2 = 0 (Y - Xb)^{T}(Y-Xb) = 0
calculated by assuming a convex shape of (Y - Xb) with varying values of b
(reasonable as the function is linear) and then calculating the point at
which the first derivative of this function is 0.
|
| Class and Description |
|---|
| LinearRegression
Given a set of independant variables a linear regressions finds the optimal
vector B such that: (Y - Xb)^2 = 0 (Y - Xb)^{T}(Y-Xb) = 0
calculated by assuming a convex shape of (Y - Xb) with varying values of b
(reasonable as the function is linear) and then calculating the point at
which the first derivative of this function is 0.
|