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  2. Stepwise regression - Wikipedia

    en.wikipedia.org/wiki/Stepwise_regression

    The main approaches for stepwise regression are: Forward selection, which involves starting with no variables in the model, testing the addition of each variable using a chosen model fit criterion, adding the variable (if any) whose inclusion gives the most statistically significant improvement of the fit, and repeating this process until none improves the model to a statistically significant ...

  3. List scheduling - Wikipedia

    en.wikipedia.org/wiki/List_scheduling

    List scheduling is a greedy algorithm for Identical-machines scheduling.The input to this algorithm is a list of jobs that should be executed on a set of m machines. The list is ordered in a fixed order, which can be determined e.g. by the priority of executing the jobs, or by their order of arrival.

  4. Feature selection - Wikipedia

    en.wikipedia.org/wiki/Feature_selection

    mRMR is a typical example of an incremental greedy strategy for feature selection: once a feature has been selected, it cannot be deselected at a later stage. While mRMR could be optimized using floating search to reduce some features, it might also be reformulated as a global quadratic programming optimization problem as follows: [ 37 ]

  5. Minimum redundancy feature selection - Wikipedia

    en.wikipedia.org/wiki/Minimum_redundancy_feature...

    This has been called maximum-relevance selection. Many heuristic algorithms can be used, such as the sequential forward, backward, or floating selections. On the other hand, features can be selected to be mutually far away from each other while still having "high" correlation to the classification variable.

  6. Look-ahead (backtracking) - Wikipedia

    en.wikipedia.org/wiki/Look-ahead_(backtracking)

    In this example, x 1 =2 and the tentative assignment x 2 =1 is considered. Forward checking only checks whether each of the unassigned variables x 3 and x 4 is consistent with the partial assignment, removing the value 2 from their domains. The simpler technique for evaluating the effect of a specific assignment to a variable is called forward ...

  7. Multivariate adaptive regression spline - Wikipedia

    en.wikipedia.org/wiki/Multivariate_adaptive...

    The backward pass has an advantage over the forward pass: at any step it can choose any term to delete, whereas the forward pass at each step can only see the next pair of terms. The forward pass adds terms in pairs, but the backward pass typically discards one side of the pair and so terms are often not seen in pairs in the final model.

  8. Forward–backward algorithm - Wikipedia

    en.wikipedia.org/wiki/Forwardbackward_algorithm

    The first pass goes forward in time while the second goes backward in time; hence the name forwardbackward algorithm. The term forwardbackward algorithm is also used to refer to any algorithm belonging to the general class of algorithms that operate on sequence models in a forwardbackward manner. In this sense, the descriptions in the ...

  9. Longest-processing-time-first scheduling - Wikipedia

    en.wikipedia.org/wiki/Longest-processing-time...

    Schedule each job in this sequence into a machine in which the current load (= total processing-time of scheduled jobs) is smallest. Step 2 of the algorithm is essentially the list-scheduling (LS) algorithm. The difference is that LS loops over the jobs in an arbitrary order, while LPT pre-orders them by descending processing time.

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