Sentence examples for supervised feature from inspiring English sources

Discriminative Subgraphs (Conf, Ratio, Gtest, HSIC): Supervised feature selection methods for graph classification based upon confidence [12], frequency ratio [16, 17, 18], G test score [37], and HSIC [20], respectively.

Care must be taken, that no information from test sets leaks into the training set, either performing certain steps (frequently supervised feature generation or selection) for the complete dataset or by "optimizing" parameters until the resulting model fits a particular test set by chance.

Supervised feature selection was performed to identify the most discriminative features (subsection 3.3).

In a preprocessing step, supervised feature selection reduces the set of features X to a subset X′ (Y is the target attribute).

Then, a supervised feature selection technique is employed to reduce feature space.

Linear Discriminant Analysis (LDA) is one of the most popular approaches for supervised feature extraction and dimension reduction.

Supervised features were successfully used in the context of image classification and retrieval, where they showed excellent results.

Models are created via the algorithm web service, which supports different types of algorithms (e.g. supervised learning, feature selection, descriptor calculation, and data cleanup).

With the proposed two-step formulation, one can integrate information theory conveniently to supervise the feature selection process while the optimal solutions can be guaranteed due to the convex optimization formulations in a generalized lasso framework.

There have been two different directions in SSL methods: 1) semi-supervised model induction approaches, which are the traditional methods and which incorporate domain knowledge from unlabeled data into the classification model during the training phase [14, 15], and 2) supervised model induction with unsupervised, possibly semi-supervised, feature learning.

Supervised methods for feature selection and classification present several pitfalls (Smialowski et al., 2009), and small datasets make analysis problematic (Martella, 2006).