Sentence examples for learning in classification from inspiring English sources

For ensemble learning in classification, the design of how the members of the ensembles interact with each other, e.g., through various combination schemes, is important for developing effective ensembles for specific problems.

Boosting is a collective term for meta-algorithms originally developed for supervised learning in classification problems (Schapire 1990; Freund 1995).

This new learning procedure's properties were assessed by using the Eidos model [Bégin and Proulx (1996)] to simulate learning in a letter-classification task.

We compared a variety of combinations of machine learning algorithms in classification experiments using compounds represented by 2 types of molecular fingerprints, for sets generated on the basis of confirmed active (from 2 activity databases) and varied numbers of assumed inactive compounds randomly selected from ZINC.

The learning step in classification task builds a model f where each attribute in x is mapped to one of the predefined discrete-valued and unordered target attribute y [ 8, 9].

The model construction process is just a standard classification model learning in data mining; for example, a decision tree is built by creating decision paths that map the conditions of the attribute values, as seen from the training samples, to the predicted classes.

Concept learning in DLs is similar to binary classification in traditional machine learning.

Transfer learning in deep neural networks becomes popular since the success of deep learning in image classification [19].

It is widely used in medical science because of its powerful learning ability in classification.

In the traditional classification learning framework, a classification task is to first train a classification model on a labeled training data.

To date, most algorithms generally treat clustering learning and classification learning in a sequential or two-step manner, i.e., first execute clustering learning to explore structures in data, and then perform classification learning on top of the obtained structural information.