<P> In speech recognition, features for recognizing phonemes can include noise ratios, length of sounds, relative power, filter matches and many others . </P> <P> In spam detection algorithms, features may include the presence or absence of certain email headers, the email structure, the language, the frequency of specific terms, the grammatical correctness of the text . </P> <P> In computer vision, there are a large number of possible features, such as edges and objects . </P> <P> In pattern recognition and machine learning, a feature vector is an n - dimensional vector of numerical features that represent some object . Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis . When representing images, the feature values might correspond to the pixels of an image, while when representing texts the features might be the frequencies of occurrence of textual terms . Feature vectors are equivalent to the vectors of explanatory variables used in statistical procedures such as linear regression . Feature vectors are often combined with weights using a dot product in order to construct a linear predictor function that is used to determine a score for making a prediction . </P>

The combination of a specific feature and a meaningful benefit is called a