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Some researchers include a metacognitive component in their definition. In this view, the Dunning–Kruger effect is the thesis that those who are incompetent in a given area tend to be ignorant of their incompetence, i.e., they lack the metacognitive ability to become aware of their incompetence.
The name weak consistency can be used in two senses. In the first sense, strict and more popular, weak consistency is one of the consistency models used in the domain of concurrent programming (e.g. in distributed shared memory, distributed transactions etc.). A protocol is said to support weak consistency if:
The best criterion is the one that has the most important role in making the decision, while the worst criterion has the opposite role. The DM then gives his/her preferences of the best criterion over all the other criteria and also his/her preferences of all the criteria over the worst criterion using a number from a predefined scale (e.g. 1 ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
[1] [3] As Graham, Knuth, and Motzkin show, this condition defines an equivalence relation, [1] the same one defined above as . [4] Corresponding to these definitions, a directed graph is called weakly connected if it has exactly one weak component. This means that its vertices cannot be partitioned into two subsets, such that all of the ...
The predual of B(H) is the trace class operators C 1 (H), and it generates the w*-topology on B(H), called the weak-star operator topology or σ-weak topology. The weak-operator and σ-weak topologies agree on norm-bounded sets in B(H). A net {T α} ⊂ B(H) converges to T in WOT if and only Tr(T α F) converges to Tr(TF) for all finite-rank ...
In probability, weak dependence of random variables is a generalization of independence that is weaker than the concept of a martingale [citation needed].A (time) sequence of random variables is weakly dependent if distinct portions of the sequence have a covariance that asymptotically decreases to 0 as the blocks are further separated in time.
The ultraweak topology can be obtained from the weak operator topology as follows. If H 1 is a separable infinite dimensional Hilbert space then B(H) can be embedded in B(H⊗H 1) by tensoring with the identity map on H 1. Then the restriction of the weak operator topology on B(H⊗H 1) is the ultraweak topology of B(H).