Sentence examples for learning regression from inspiring English sources

It relies on an original machine learning regression approach.

A number of computer vision problems such as facial age estimation, crowd counting and pose estimation can be solved by learning regression mapping on low-level imagery features.

Four machine learning regression algorithms including Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (k-NN), and Artificial Neural Network (ANN) were evaluated and compared to the commonly used Multiple Linear Regression (MLR) method for both live and total sawgrass biomass estimation.

That is, learning regression coefficients can help to refine the prior networks, and vice versa.

These features are used as input to a Random Forest (RF) machine learning regression model trained to select the loop template with the lowest predicted distance from the target loop among a list of putative ones.

The paper also provides a comparison between machine learning regression and classification algorithms.

We train a set of Gaussian processes to learn regression from SCD to each dimension of the weak poses separately.

Our approach is somewhat similar to the work of Agius et al. (2010) and Annala et al. (2011), who also learn regression models from PBM data.

This allows us to learn regression models that take into account k-mer occurrences at specific positions relative to the core of the binding site, as opposed to k-mers occurrences along the probes as done previously in Annala et al. (2011).

We have presented a new approach for learning regression-based models of protein DNA binding specificity from quantitative TF binding data, using SVR with feature selection.

In the machine learning domain, regression is a supervised method, which outputs continuous values (instead of discrete values such as classes, categories, labels, etc).