Algebraic multigrid support vector machines
The support vector machine is a flexible optimization-based technique widely used for classification problems. In practice, its training part becomes computationally expensive on large-scale data sets because of such reasons as the complexity and number of iterations in parameter fitting methods, underlying optimization solvers, and nonlinearity of kernels. We introduce a fast multilevel framework for solving support vector machine models that is inspired by the algebraic multigrid. Significant improvement in the running has been achieved without any loss in the quality. The proposed technique is highly beneficial on imbalanced sets. We demonstrate computational results on publicly available and industrial data sets.
Code (1)
Tasks
General ClassificationSimilar Papers 제목 키워드 기반
Reducing operator complexity in Algebraic Multigrid with Machine Learning Approaches
We propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in algebraic multigrid (AMG) methods, addressing the well-known issue of increasing operator complexity. Guided b…
Tverberg's theorem and multi-class support vector machines
We show how, using linear-algebraic tools developed to prove Tverberg's theorem in combinatorial geometry, we can design new models of multi-class support vector machines (SVMs). These supervised learning protocols requi…
Automated Grammar-based Algebraic Multigrid Design With Evolutionary Algorithms
Although multigrid is asymptotically optimal for solving many important partial differential equations, its efficiency relies heavily on the careful selection of the individual algorithmic components. In contrast to rece…
M2NO: Multiresolution Operator Learning with Multiwavelet-based Algebraic Multigrid Method
Solving partial differential equations (PDEs) effectively necessitates a multi-scale approach, particularly critical in high-dimensional scenarios characterized by increasing grid points or resolution. Traditional method…
Operator learningSuper-ResolutionAlgebraically Explainable Controllers: Decision Trees and Support Vector Machines Join Forces
Recently, decision trees (DT) have been used as an explainable representation of controllers (a.k.a. strategies, policies, schedulers). Although they are often very efficient and produce small and understandable controll…