Deep Regression. Deep regression forests and deep Gaussian process have recently been applied to many computer vision problems with remarkable success. When Y is continous we call the resulting model a regression model.
Ive gotten quite a few requests recently for a examples using neural networks for regression rather than classification and b examples using time series. Contrast this with a classification problem where the aim is to select a class from a list of classes for example where a picture contains an apple or an orange recognizing which fruit is in the picture. Mar 19 2021 A Lawrence Livermore National Laboratory team has developed a new deep reinforcement learning framework for a type of discrete optimization called symbolic regression showing it could outperform several common methods including commercial software gold standards on benchmark problems.
Contrast this with a classification problem where the aim is to select a class from a list of classes for example where a picture contains an apple or an orange recognizing which fruit is in the picture.
We address this issue by leveraging the low-rank property of learnt feature vectors produced from deep neural networks DNNs with the closed-form solution provided in kernel ridge regression KRR. Neural network models for multi-output regression tasks can be easily defined and evaluated using the Keras deep learning library. Deep regression trackers do not perform as well as discriminative correla-tion lters DCFs trackers. Y can be continuous discrete or mixed.
