SEMIPARAMETRIC VECTOR MEM

Semiparametric Support Vector and Linear Programming Machines

Semiparametric models are useful tools in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. We extend two learning algorithms Support Vector machines and Linear Programming machines to this case and give experimental results for SV machines.

Reducing samples for accelerating multikernel semiparametric support vector regression

In this paper, the reducing samples strategy instead of classical m-support vector regression (m-SVR), viz. single kernel m-SVR, is utilized to select training samples for admissible functions so as to curtail the computational complexity. The proposed multikernel learning algorithm, namely reducing samples based multikernel semiparametric support vector regression (RS-MSSVR), has an advantage ...

Decomposition Algorithms for Training Large-Scale Semiparametric Support Vector Machines

We describe a method for solving large-scale semiparametric support vector machines (SVMs) for regression problems. Most of the approaches proposed to date for large-scale SVMs cannot accommodate the multiple equality constraints that appear in semiparametric problems. Our approach uses a decomposition framework, with a primal-dual algorithm to find an approximate saddle point for the min-max f...

Sign Problem and Mem * †

The sign problem is notorious in Monte Carlo simulations of lattice QCD with the finite density, lattice field theory (LFT) with a θ term and quantum spin models. In this report, to deal with the sign problem, we apply the maximum entropy method (MEM) to LFT with the θ term and investigate to what extent the MEM is applicable to this issue. Based on this study, we also make a brief comment abou...

Mem. S.A.It. Vol.