Examples of 'feature vectors' in a sentence
Meaning of "feature vectors"
feature vectors - In the context of data analysis and machine learning, a feature vector represents a numerical representation of specific features or characteristics of an object or data point. It is used to mathematically describe the properties or attributes of the object. Feature vectors are often used as inputs in algorithms and models to make predictions, classify data, or analyze patterns
How to use "feature vectors" in a sentence
Basic
Advanced
feature vectors
Class is a set of feature vectors considered to be similar.
A representation of the people as big feature vectors.
Such generated feature vectors are stored in a feature vector database.
These parameters are then saved as feature vectors.
The set of all possible feature vectors constitutes a feature space.
It is known to describe a spoken word by a series of feature vectors.
Each of the representative feature vectors represents an image feature cluster.
The plus signs indicate the query feature vectors.
Feature vectors are then extracted from the normalized local electrograms.
The time sequence consists of five such feature vectors.
As live feature vectors are calculated they are added to the stored training data.
It calculates the cosine of the angle between two feature vectors.
The extraction of the text feature vectors includes the following steps.
The extractedmotifs are used as descriptors to encode proteins into feature vectors.
The global feature vectors may also be compared with stored ranges.
See also
The audio fingerprint is then computed based on the feature vectors.
The feature vectors are created and stored in previously specified time intervals.
Determining the identity of an animal may also be performed by comparing feature vectors.
The recognition program analyses a sequence of feature vectors in terms of a number of models.
The data for the feature vectors is collected by a monitoring component of the agent.
With one derived from a new set of input acoustic feature vectors.
Rformula allows you to generate feature vectors along with their feature labels.
Templates are preferably positioned near the arithmetic means of clusters of associated feature vectors.
The set of all feature vectors obtained is clustered by means of a fuzzy clustering algorithm.
The feature matrix consists of a number of feature vectors that each correspond to an egg.
Corresponding feature vectors are formed from the measured features for each sample.
The agent collects the packet data and creates feature vectors continuously.
A detailed description of the feature vectors is provided below with respect to performed simulations.
Each of the upper representative vectors may represent a plurality of representative feature vectors.
Selecting a group of representative feature vectors corresponds to selecting a representative cluster corresponding to the group.
The neural gas is a simple algorithm for finding optimal data representations based on feature vectors.
Other query feature vectors extracted from the query image may be calculated in parallel.
The search for such distinctive time invariant feature vectors will be described below.
The enrolment feature vectors are usually extracted from the utterances provided for the enrolment.
The distance between the detectors and the feature vectors is computed by the hamming distance.
The inputs to the MLP are conventional acoustic feature vectors.
Let x and y be any two n dimensional feature vectors assigned to the same cluster Ci.
Feature vectors are extracted out of this data serving as an input for the AIS.
The speaker dependent transform matrix comprises a representative sequence of feature vectors for a particular word.
Subtracting two feature vectors gives a vector of N difference values.
A close match between a template and the input acoustic feature vectors yields a very low matching score.
Multivariate Gaussian distributions are commonly used to model the multidimensional input feature vectors.
There would thus be produced N such global feature vectors for each test specimen.
The used feature vectors are given below,.
The utterance X is the series of feature vectors xj.
In other embodiments, feature vectors may comprise a variety of other suitable measurements.
In another embodiment, the processor reduces size of the feature vectors.
If the feature vectors belong to different classes, the three variables are fully independent.
The clusters into which the image feature vectors 22 are classified may be multistage.
The analyzing portion 1 transforms input speech into a time sequence of feature vectors.
You'll also be interested in:
Examples of using Feature
Show more
Feature will be installed on local hard drive
The callback feature can not be activated
Feature either the boobs or the legs
Examples of using Vectors
Show more
The potential vectors for trafficking are limited
And a distress call with locator vectors
It typically vectors through unsanitary handling practices
