Definitions for Feature Vector
fea·ture vec·tor

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1. feature vector

   In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression.

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1. Feature vector

   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, when representing texts perhaps to term occurrence frequencies. 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. The vector space associated with these vectors is often called the feature space. In order to reduce the dimensionality of the feature space, a number of dimensionality reduction techniques can be employed. Higher-level features can be obtained from already available features and added to the feature vector, for example for the study of diseases the feature 'Age' is useful and is defined as Age = 'Year of death' - 'Year of birth'. This process is referred to as feature construction. Feature construction is the application of a set of constructive operators to a set of existing features resulting in construction of new features. Examples of such constructive operators include checking for the equality conditions {=, ≠}, the arithmetic operators {+,−,×, /}, the array operators {max, min, average} as well as other more sophisticated operators, for example count that counts the number of features in the feature vector S satisfying some condition C or, for example, distances to other recognition classes generalized by some accepting device. Feature construction has long been considered a powerful tool for increasing both accuracy and understanding of structure, particularly in high-dimensional problems. Applications include studies of disease and emotion recognition from speech.

How to pronounce Feature Vector?

1. Alex

   US English

   David

   US English

   Mark

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   Daniel

   British

   Libby

   British

   Mia

   British

   Karen

   Australian

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   Australian

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   Indian

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   Neerja

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   Oliver

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   South African

How to say Feature Vector in sign language?

Numerology

1. Chaldean Numerology

   The numerical value of Feature Vector in Chaldean Numerology is: 4

2. Pythagorean Numerology

   The numerical value of Feature Vector in Pythagorean Numerology is: 6

References

1. ^ Wikipedia
   https://en.wikipedia.org/wiki/Feature_Vector
2. ^ Wikidata
   https://www.wikidata.org/w/index.php?search=Feature Vector