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Clarifier removes solids from Liquid and Clarifier removes solids from Gas

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Difference between a oil purifier and oil clarifier?

Basically, a clarifier is attached just after the purifier in the fuel line. While the main function of a purifier is to separate the dissolved water, impurities and sludge from the fuel oil, a clarifier removes any solid foreign material that is not removed from the oil after it passes through the purifier.


What is the difference between a neural network designed as an Approximator and the one designed as a Classifier?

The network used is a feed-forward network (back-propagated just mean that the back propagation algorithmis used for training). It is a classifier: it classifies if a pixel is part of the rotten or healthy area.


What is nonparametric classifier?

A nonparametric classifier is a kind of classifier that can work with unknown density function of the classes of a dataset.


What are the two types of Classification Authority?

Orignial classifier and derivative classifier


What are the qualifications of a classifier at the national food authority?

what are the qualifications of a classifier at the national food authority


What do you mean by classifiers in unified modeling language?

Classifier is an abstract UML metaclass to support classification of instances according to their features. Classifier describes a set of instances that have common features. A feature declares a structural (properties) or behavioral (operations) characteristic of instances of classifiers.More formally, in UML 2.2 Classifier is (extends):NamespaceTypeRedefinable ElementNamespace is an element in a model that can own (contain) other named elements. As a Namespace, classifier can have features.Type represents a set of values. A typed element that has this type is constrained to represent values within this set. As a Type, classifier can own generalizations, thereby making it possible to define generalization relationships to other classifiers.Redefinable Element is an element that, when defined in the context of a classifier, can be redefined more specifically or differently in the context of another classifier that specializes (directly or indirectly) the context classifier. As a Redefinable Element, it is possible for classifier to redefine nested classifiers.Some examples (subclasses) of Classifiersin UML 2.2 are:ClassInterfaceAssociationDataTypeActor (subclass of Behaviored Classifier)Use Case (subclass of Behaviored Classifier)ArtifactComponent (subclass of Class)Signal


What is the purpose of a Lamella clarifier?

Lamella Clarifier is a particular brand of inclined plate clarifier. Their products are used to clarify and recycle by separating particles and dirt from unclean water.


What is a classifier of a noun?

Yes, the word 'classifier' is a noun, a word for one who classifies (a person); a word for a device for separating solids of different characteristics (a thing).


A derivative classifier is the person who?

an OCA previously classified


How long to wait after adding clarifier to pool to add other chemicals?

It is generally recommended to wait at least 24 hours after adding a pool clarifier before adding other chemicals to allow the clarifier to work effectively. This will ensure that the clarifier has sufficient time to settle and improve water clarity before introducing additional chemicals.


What are the advantages of flocculent vs clarifier for clearing up pool water?

From what I gather from reading the back of the bottle of Clarifier that I use in my pool a Clarifier is a Flocculent. The clarifier contains a polymer that attracts the smaller particles floating around in the pool that are too small to be filtered through some filtration systems. As the clarifier attracts the smaller particles it becomes large enough for the filtration system to filter it out of the water thus clariying the pool water.


What is the proof that the Bayes classifier is the optimal classifier in terms of minimizing the classification error?

The Bayes classifier is considered optimal because it minimizes the classification error by making decisions based on the probability of each class given the input data. This is supported by mathematical proofs and theory in the field of statistics and machine learning.