This tutorial provides practical guidance for researchers seeking to extend DP and RL techniques to larger domains through linear value function approximation. The practical algorithms and empirical ...

You can also use linear models for classification. Some linear models transform the weighted sum into a more convenient form. For example, logistic regression plugs the weighted sum into the logistic ...

In this case linear regression appears to be a reasonable choice. There is significant noise in the data, but the underlying relationship seems mostly linear. The models for the first two graphs ...

In this tutorial you can learn how the gradient descent algorithm works and implement it from scratch in python. First we look at what linear regression is, then we define the loss function. We learn ...

Performance Analysis of Linear Codes under Maximum-Likelihood Decoding: A Tutorial focuses on the performance evaluation of linear codes under optimal maximum-likelihood (ML) decoding. Though the ML ...

The minimization of a convex function of variables subject to linear inequalities is discussed briefly in general terms. Dantzig's Simplex Method is extended to yield finite algorithms for minimizing ...