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  2. Ranking SVM - Wikipedia

    en.wikipedia.org/wiki/Ranking_SVM

    In machine learning, a ranking SVM is a variant of the support vector machine algorithm, which is used to solve certain ranking problems (via learning to rank). The ranking SVM algorithm was published by Thorsten Joachims in 2002. [1] The original purpose of the algorithm was to improve the performance of an internet search engine. However, it ...

  3. Learning to rank - Wikipedia

    en.wikipedia.org/wiki/Learning_to_rank

    Learning to rank [1] or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. [2] Training data may, for example, consist of lists of items with some partial order specified between items in ...

  4. VIKOR method - Wikipedia

    en.wikipedia.org/wiki/VIKOR_method

    The results are three ranking lists. Step 5. Propose as a compromise solution the alternative A(1) which is the best ranked by the measure Q (minimum) if the following two conditions are satisfied: C1. “Acceptable Advantage”: Q(A(2) – Q(A(1)) >= DQ where: A(2) is the alternative with second position in the ranking list by Q; DQ = 1/(J-1). C2.

  5. Relief (feature selection) - Wikipedia

    en.wikipedia.org/wiki/Relief_(feature_selection)

    Rather than repeating the algorithm m times, implement it exhaustively (i.e. n times, once for each instance) for relatively small n (up to one thousand). Furthermore, rather than finding the single nearest hit and single nearest miss, which may cause redundant and noisy attributes to affect the selection of the nearest neighbors, ReliefF searches for k nearest hits and misses and averages ...

  6. Lagrange multiplier - Wikipedia

    en.wikipedia.org/wiki/Lagrange_multiplier

    As a result, the method of Lagrange multipliers is widely used to solve challenging constrained optimization problems. Further, the method of Lagrange multipliers is generalized by the Karush–Kuhn–Tucker conditions , which can also take into account inequality constraints of the form h ( x ) ≤ c {\displaystyle h(\mathbf {x} )\leq c} for a ...

  7. Combinatorial optimization - Wikipedia

    en.wikipedia.org/wiki/Combinatorial_optimization

    A minimum spanning tree of a weighted planar graph.Finding a minimum spanning tree is a common problem involving combinatorial optimization. Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, [1] where the set of feasible solutions is discrete or can be reduced to a discrete set.

  8. LU decomposition - Wikipedia

    en.wikipedia.org/wiki/LU_decomposition

    The cost of solving a system of linear equations is approximately floating-point operations if the matrix has size . This makes it twice as fast as algorithms based on QR decomposition , which costs about 4 3 n 3 {\textstyle {\frac {4}{3}}n^{3}} floating-point operations when Householder reflections are used.

  9. Multi-objective optimization - Wikipedia

    en.wikipedia.org/wiki/Multi-objective_optimization

    Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.