# Least Squares

## Main.LeastSquares History

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Least squares method for [[online linear regression]] is a method which finds the best fitted predictor for the past data, and then predicts following this predictor. It finds the argument of $\min_\theta \left(\sum_{t=1}^T (\theta' x_t - y_t)^2)$ after $T$ steps.

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Least squares method for [[online linear regression]] is a method which finds the best fitted predictor for the past data, and then predicts following this predictor. It finds the argument of $\min_\theta \left(\sum_{t=1}^T (\theta' x_t - y_t)^2 \right)$ after $T$ steps.

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Least squares method for [[online linear regression]] is a method which finds the best fitted predictor for the past data, and then predicts following this predictor. It finds the argument of $\min_\theta \left(\sum_{t=1}^T (\theta' x_t - y_t)^2)$ after $T$ steps.

See more [[http://en.wikipedia.org/wiki/Least_squares]].

See more [[http://en.wikipedia.org/wiki/Least_squares]].