Sentence examples for form of features from inspiring English sources

This paper deals with the problem of extracting information from non-stationary signals in the form of features that can be used for effective decision-making in both data analysis and machine learning for automatic classification systems.

The first approach operates at the front-end via the exploration of discriminative information in speech in the form of features (e.g., voice source, spectro-temporal, prosodic, high-level) [1]-[6].

Our framework has some similarities to graph-based label propagation in the sense that a graph is built based on proximity of unlabeled conversations, and then it is used to propagate confidences (in the form of features) to the labeled data, based on which perceptron trains a discriminative model.

In contrast, our proposed approach is inductive, and can integrate multiple diverse sources of data in the form of features, including the different biological networks.

We evaluate different configurations of the features with different orders of offset conjunction (adding context in the form of features of the last p and next p tokens, where p is the order of the offset conjunction) as well as the order of the CRF, which includes information from the last q labels (q is the order of the CRF).

Despite of the analysis given here for IUPAC entities, the open question remains, in which cases a representation of context information on the labels should be preferred in comparison to a representation of context information in the text, in form of features, used here by incorporating offset conjunction.

Some form of feature selection will improve classification results and improve the efficiency of the system.

Usually, some form of feature (gene) selection is adopted in the formation of the prediction rule.

A Bernoulli NB classifier requires some form of feature selection or else its accuracy will be low.

The network has previous in reviving series in the form of feature films, releasing two Sex and the City spin-off movies in 2008 and 2010, and an Entourage film earlier this year.

Many classification problems involve features whose specificity demand some form of feature space transformation (preprocessing) coupled with post-processing consensus analysis in order to accomplish a successful discrimination between different classes.