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The entity–control–boundary (ECB), or entity–boundary–control (EBC), or boundary–control–entity (BCE) is an architectural pattern used in use-case–driven object-oriented programming that structures the classes composing high-level object-oriented source code according to their responsibilities in the use-case realization.
Decision boundaries are not always clear cut. That is, the transition from one class in the feature space to another is not discontinuous, but gradual. This effect is common in fuzzy logic based classification algorithms, where membership in one class or another is ambiguous. Decision boundaries can be approximations of optimal stopping boundaries.
In machine learning, one-class classification (OCC), also known as unary classification or class-modelling, tries to identify objects of a specific class amongst all objects, by primarily learning from a training set containing only the objects of that class, [1] although there exist variants of one-class classifiers where counter-examples are used to further refine the classification boundary.
Class diagram showing generalization between the superclass Person and the two subclasses Student and Professor. The generalization relationship—also known as the inheritance or "is a" relationship—captures the idea of one class, the so-called subclass, being a specialized form of the other (the superclass, super type, or base class). Where ...
Classes can be derived from one or more existing classes, thereby establishing a hierarchical relationship between the derived-from classes (base classes, parent classes or superclasses) and the derived class (child class or subclass) . The relationship of the derived class to the derived-from classes is commonly known as an is-a relationship. [21]
This is an exact sequence relating the mapping class group of surfaces with the same genus and boundary but a different number of punctures. It is a fundamental tool which allows to use recursive arguments in the study of mapping class groups.
The Stiefel–Whitney classes are the Steenrod squares of the Wu classes, defined by Wu Wenjun in 1947. [3] Most simply, the total Stiefel–Whitney class is the total Steenrod square of the total Wu class: Sq ( v ) = w {\displaystyle \operatorname {Sq} (v)=w} .
Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set.