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In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to). [1]
An example of a hierarchical clustering algorithm is BIRCH, which is particularly good on bioinformatics for its nearly linear time complexity given generally large datasets. [27] Partitioning algorithms are based on specifying an initial number of groups, and iteratively reallocating objects among groups to convergence.
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. [1] Choosing informative, discriminating, and independent features is crucial to produce effective algorithms for pattern recognition, classification, and regression tasks.
In this case, the learning-to-rank problem is approximated by a classification problem — learning a binary classifier (,) that can tell which document is better in a given pair of documents. The classifier shall take two documents as its input and the goal is to minimize a loss function L ( h ; x u , x v , y u , v ) {\displaystyle L(h;x_{u},x ...
The first span starts with a special token [CLS] (for "classify"). The two spans are separated by a special token [SEP] (for "separate"). After processing the two spans, the 1-st output vector (the vector coding for [CLS] ) is passed to a separate neural network for the binary classification into [IsNext] and [NotNext] .
In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...
Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite constraints over variables , which is solved by constraint satisfaction methods.
For example, a module may require functions of type a-> b, but it is more convenient to write functions of type a * c-> b where there is a fixed relationship between the objects of type a and c. A function of type c-> (a * c-> b)-> a-> b can factor out this commonality. This is an example of the adapter pattern. [citation needed]