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In mathematics, statistics, finance, [1] and computer science, particularly in machine learning and inverse problems, regularization is a process that converts the answer of a problem to a simpler one. It is often used in solving ill-posed problems or to prevent overfitting. [2]
Parity learning is a problem in machine learning. An algorithm that solves this problem must find a function ƒ, given some samples (x, ƒ(x)) and the assurance that ƒ computes the parity of bits at some fixed locations. The samples are generated using some distribution over the input.
The multiplicative weights method is usually used to solve a constrained optimization problem. Let each expert be the constraint in the problem, and the events represent the points in the area of interest. The punishment of the expert corresponds to how well its corresponding constraint is satisfied on the point represented by an event. [1]
The network is pruned, and then kept if it is an improvement over the previous model. Dilution and dropout both refer to an iterative process. The pruning of weights typically does not imply that the network continues learning, while in dilution/dropout, the network continues to learn after the technique is applied.
Empirically, for machine learning heuristics, choices of a function that do not satisfy Mercer's condition may still perform reasonably if at least approximates the intuitive idea of similarity. [6] Regardless of whether k {\displaystyle k} is a Mercer kernel, k {\displaystyle k} may still be referred to as a "kernel".
In the statistical learning theory framework, an algorithm is a strategy for choosing a function: given a training set = {(,), …, (,)} of inputs and their labels (the labels are usually ).
QLattice is a quantum-inspired simulation and machine learning technology that helps search through an infinite list of potential mathematical models to solve a problem. [13] [14] Evolutionary Forest is a Genetic Programming-based automated feature construction algorithm for symbolic regression. [15] [16]
QUBO is an NP hard problem, and for many classical problems from theoretical computer science, like maximum cut, graph coloring and the partition problem, embeddings into QUBO have been formulated. [2] [3] Embeddings for machine learning models include support-vector machines, clustering and probabilistic graphical models. [4]