Examples of 'feature vector' in a sentence
Meaning of "feature vector"
A feature vector is an n-dimensional vector of numerical features that represent some object. It is commonly used in machine learning and pattern recognition to describe the characteristics of data instances
How to use "feature vector" in a sentence
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feature vector
Again xt is a feature vector in the time interval t.
It is of same size as the associated feature vector.
Creating a feature vector descriptor to run facial recognition.
The result is known as a feature descriptor or feature vector.
Going to do is define a feature vector x corresponding to this image as follows.
This vector is referred to as a feature vector.
This gives a feature vector for the entire window.
A set of numeric features can be conveniently described by a feature vector.
Each neuron is associated with a feature vector referred to as observation.
A feature vector is then extracted from the normalized electrogram.
The description of the element of a set is defined by a feature vector.
A feature vector is then derived from the measured values.
Preliminary suggestions are given to plan a new feature vector.
Such generated feature vectors are stored in a feature vector database.
Going to do is unroll all of these pixel values into an input feature vector x.
See also
So now my feature vector.
In speech or speaker recognition each observation generally corresponds to a feature vector.
Each segment contributes to the feature vector that describes de keypoint.
Several different signals could generate the same feature vector.
A representative feature vector is then extracted from the representative electrogram.
The feature data comprises a feature vector for each epoch.
A feature vector may be based on at least two of the features described herein.
The three metrics provide a feature vector for each timeslot.
A feature vector x of dimension k is estimated from sets of successive windows of sensor data.
The private biometric feature vector can be used in one of two ways.
This allows easier and more efficient processing of the feature vector.
The data for this feature vector is then applied as input to a classifier.
A frame classification procedure performs a classification decision based on this feature vector.
It is anticipated that the concatenated reduced feature vector will be generated periodically.
Statistical measurements of the features are taken and combined to form a feature vector.
Each component of the feature vector then only depends on some frequency bands.
One posterior vector is output for each input acoustic feature vector.
Each cluster is represented by a prototypical feature vector that is determined by the clustering algorithm.
Signal discrimination accuracy is proportional to the time bandwidth product of the feature vector.
The motion is then analysed and a feature vector generated representative of the motion.
The following description is made of the processing for a given query feature vector.
The spectrum is then transformed into a feature vector that is compared to the prototypes.
This feature vector is then used to derive the blood sugar level of the patient.
Regional classification of the tissue property is then performed based on the representative feature vector.
The extracted features are then combined in a feature vector for use with the classifier.
The ph feature vector and a binary acoustic mask are proposed for the speech emotion classication.
These simple audio features are often combined into a single large music similarity feature vector.
Each feature vector is a row vector containing one or more features for the given egg.
MinMaxScaler rescales each feature vector into a fixed range.
Allows a response variable Y to be modeled as a linear function of multidimensional feature vector.
Generating a feature vector representative of said input ;.
The resulting series of coefficients from the FFT is called a feature vector.
The feature vector is then input to the classifier 6.
The SVM then computes an SVM channel seizure output for each feature vector.
Generating a feature vector from the extracted features ;.
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