Examples of 'vector machines' in a sentence
Meaning of "vector machines"
Vector machines refers to a type of machine learning algorithm that is used for classification and regression tasks. It works by identifying the best possible decision boundary to separate data points into different categories. This algorithm is commonly used in various fields such as image recognition, natural language processing, and bioinformatics
How to use "vector machines" in a sentence
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vector machines
Neural nets and support vector machines.
Support vector machines often use the kernel trick.
We first consider the one of support vector machines.
Support vector machines also typically work well for.
Time series prediction using support vector machines.
Multiple support vector machines are trained to cover all possible combinations.
Geometric classifiers such as support vector machines are gradient models.
Support vector machines are algorithms that are based on statistical learning theory.
Classification using support vector machines and variables selection.
One such approach is to use an ensemble of support vector machines.
Support vector machines are supervised learning models used for classification and regression analysis.
An example would be the tolerance hyperparameter for errors in support vector machines.
The use of support vector machines is a known technique for classification and separation of data.
The first classifier and the second classifier may be support vector machines.
Support vector machines and the Bayes rule.
See also
An algorithm for dimensionality reduction associated with a classification by support vector machines has been developed.
Support Vector Machines are based on the concept of decision planes that define decision boundaries.
He has made particular contributions to support vector machines and kernel PCA.
Support Vector Machines are particularly suited to handle such tasks.
Selected algorithms for the text classification were support vector machines and a naive Bayes classifier.
Support Vector Machines is one of the most classical representant.
See the article on Support Vector Machines for more details.
In another embodiment, the processor executes an ensemble of support vector machines.
Finally, support vector machines are trained using the selected features.
Moreover, one class classifiers and support vector machines are evaluated.
Among the vector machines it is according to the vector operations ' realization vector pipeline, matrix.
Object-based classification and support vector machines were used in the guided classification.
Investment signals via machine learning classifiers, such as neural networks and support vector machines.
Eleven classification methods other than multi-class support vector machines were tested with the smaller dataset.
In a further embodiment, the processor determines a pose according to votes from different support vector machines.
Support Vector Machines ( SVMs ) are machine learning supervised techniques relatively new for classification uses.
Introduction to machine learning algorithms such as decision trees, support vector machines or random forests.
The Support Vector Machines will instead choose this decision boundary, which I am drawing in black.
Nguyen et al . Optimal feature selection for support vector machines.
These include logistic regression and support vector machines ( as implemented in Azure Machine Learning ).
Support vector machines ( SVMs ) are a set of related supervised learning.
LIBLINEAR: A library implementing linear support vector machines and logistic regression. Training is extremely fast.
The hinge loss is used for " maximum-margin " classification, most notably for support vector machines SVMs.
Kernel machines or Support Vector Machines ( SVM ) can help with both goals.
Classification tools - linear classifiers, neural classifiers, support vector machines - are detailed.
Support Vector Machine ( SVM ) Support Vector Machines ( SVMs ) is a discriminative classifier formally defined by a separating hyperplane.
The classification is finally made by support vector machines ( SVM ).
One-class support vector machines.
Machine learning algorithms such as Regression, clustering, decision-tree, support vector machines etc.
The data are then classified using Support Vector Machines ( SVMs ).
Lastly, but certainly not leastly, Support Vector Machines.
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