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  2. Normalization (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Normalization_(machine...

    For instance, a popular choice of feature scaling method is min-max normalization, where each feature is transformed to have the same range (typically [,] or [,]). This solves the problem of different features having vastly different scales, for example if one feature is measured in kilometers and another in nanometers.

  3. NACK-Oriented Reliable Multicast - Wikipedia

    en.wikipedia.org/wiki/NACK-Oriented_Reliable...

    A NORM node refers to an individual node taking part in a NORM session. Each node has a unique identifier. When a node transmits a NORM message, this identifier is noted as the source_id. A NORM instance refers to an individual node in the context of a continuous segment of a NORM session. When a node joins a NORM session, it has a unique node ...

  4. Batch normalization - Wikipedia

    en.wikipedia.org/wiki/Batch_normalization

    The explanation made in the original paper [1] was that batch norm works by reducing internal covariate shift, but this has been challenged by more recent work. One experiment [2] trained a VGG-16 network [5] under 3 different training regimes: standard (no batch norm), batch norm, and batch norm with noise added to each layer during training ...

  5. Row- and column-major order - Wikipedia

    en.wikipedia.org/wiki/Row-_and_column-major_order

    Note how the use of A[i][j] with multi-step indexing as in C, as opposed to a neutral notation like A(i,j) as in Fortran, almost inevitably implies row-major order for syntactic reasons, so to speak, because it can be rewritten as (A[i])[j], and the A[i] row part can even be assigned to an intermediate variable that is then indexed in a separate expression.

  6. Multiple instance learning - Wikipedia

    en.wikipedia.org/wiki/Multiple_Instance_Learning

    Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled bags, each containing many instances. In the simple case of multiple-instance binary classification, a bag may be labeled negative if all the instances in it are negative. On the other hand, a bag is labeled positive if there is at ...

  7. Non-negative matrix factorization - Wikipedia

    en.wikipedia.org/wiki/Non-negative_matrix...

    NMF can be seen as a two-layer directed graphical model with one layer of observed random variables and one layer of hidden random variables. [47] NMF extends beyond matrices to tensors of arbitrary order. [48] [49] [50] This extension may be viewed as a non-negative counterpart to, e.g., the PARAFAC model.

  8. Matrix norm - Wikipedia

    en.wikipedia.org/wiki/Matrix_norm

    Suppose a vector norm ‖ ‖ on and a vector norm ‖ ‖ on are given. Any matrix A induces a linear operator from to with respect to the standard basis, and one defines the corresponding induced norm or operator norm or subordinate norm on the space of all matrices as follows: ‖ ‖, = {‖ ‖: ‖ ‖ =} = {‖ ‖ ‖ ‖:} . where denotes the supremum.

  9. 5G network slicing - Wikipedia

    en.wikipedia.org/wiki/5G_network_slicing

    The network function layer is in charge of the creation of each network slice according to service instance requests coming from the upper layer. It is composed of a set of network functions that embody well-defined behaviors and interfaces. Multiple network functions are placed over the virtual network infrastructure and chained together to ...