Examples of using Classifiers in English and their translations into Greek
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Colloquial
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Official
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Medicine
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Ecclesiastic
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Financial
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Official/political
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Computer
Base Classifiers.
Linear and non-linear classifiers.
Various classifiers, such as.
Scripts and predefined classifiers.
There are 5 ANN classifiers in the project corresponding to.
Add high efficiency classifiers.
Programming classifiers with the library Scikit-learn.
Flow identification, Packet Classifiers and Filters.
One class classifiers based on artificial immune systems.
Algorithms with this basic setup are known as linear classifiers.
Derived Classifiers.
Spiral Classifiers are usually suitable for the following situations.
Classification: after the materials are crushed, they will be discharged into self-diffluent classifiers.
Then it uses the classifiers to predict financial performance.
The basic cardinal numbers(transcribed into Latin script IPA) are:The numerals are used with numeral classifiers.
(4) High classification efficiency(50% higher than classifiers in traditional grinding mills).
Other classifiers work by comparing observations to previous observations by means of a similarity or distance function.
Python Programming for Machine Learning. Programming classifiers with the library Scikit-learn.
Now, Supervision includes Microsoft Teams content andadditional capabilities to flag sensitive data types and offensive language classifiers.
In addition to addressing the classifiers for the implementation of these projects, and the award of tenders on them.
Simple, non-technical, learn-by-example interface- train high quality classifiers by simply drawing on the images.
With machine learning methods, so-called classifiers were built that can distinguish transcriptomes coming from“young” versus“old” donors.
Since many classification methods have been developed specifically for binary classification,multiclass classification often requires the combined use of multiple binary classifiers.
With machine-learning methods,they built so-called classifiers that can distinguish transcriptomes from"young" versus"old" donors.
Recently, careful analysis of the Bayesian classification problem has shown that there are some theoretical reasons for the apparently unreasonable efficacy of naive Bayes classifiers.
Depending on the precise nature of the probability model, naive Bayes classifiers can be trained very efficiently in a supervised learning setting.
Probabilistic classifiers can be more effectively incorporated into larger machine learning tasks, in a way that partially or completely avoids the problem of error propagation.
And it said that the Supervision tool within Advanced Compliance now includes Microsoft Teams content andadditional features to flag sensitive data types and“offensive language” classifiers.
Recent readout experiments have demonstrated that statistical classifiers(e.g. support vector machines) can be trained to classify objects based on the responses of a small number of TE neurons.
Predictor is a tool that uses financial statements, income statements, balance sheets and cashflow statements andcreates powerful pattern classifiers based on that data.