10,546 research outputs found
Using SVM to combine global heuristics for the Standard Quadratic Problem
No full textThe Standard Quadratic Problem (StQP) is an NP-hard problem with many local minimizers (stationary
points). In the literature, heuristics based on unconstrained continuous non-convex formulations have been
proposed (Bomze & Palagi, 2005; Bomze, Grippo, & Palagi, 2012) but none dominates the other in terms of
best value found. Following (Cassioli, DiLorenzo, Locatelli, Schoen, & Sciandrone, 2012) we propose to use
Support Vector Machines (SVMs) to define a multistart global strategy which selects the “best” heuristic.
We test our method on StQP arising from the Maximum Clique Problem on a graph which is a challenging
combinatorial problem. We use as benchmark the clique problems in the DIMACS challenge
Laura Bocciolini Palagi, La trottola di Dioniso. Motivi dionisiaci nel VII libro dell’Eneide. Bologne, Pàtron, 2007
No full textVan haeperen Françoise. Laura Bocciolini Palagi, La trottola di Dioniso. Motivi dionisiaci nel VII libro dell’Eneide. Bologne, Pàtron, 2007. In: L'antiquité classique, Tome 79, 2010. p. 446
Laura Bocciolini Palagi, La trottola di Dioniso. Motivi dionisiaci nel VII libro dell’Eneide. Bologne, Pàtron, 2007
No full textVan haeperen Françoise. Laura Bocciolini Palagi, La trottola di Dioniso. Motivi dionisiaci nel VII libro dell’Eneide. Bologne, Pàtron, 2007. In: L'antiquité classique, Tome 79, 2010. p. 446
Laura Bocciolini Palagi, Epistolario apocrifo di Seneca e San Paolo, 1985
No full textMaraval Pierre. Laura Bocciolini Palagi, Epistolario apocrifo di Seneca e San Paolo, 1985. In: Revue d'histoire et de philosophie religieuses, 66e année n°3, Juillet-septembre 1986. pp. 344-345
Global optimization issues in deep network regression: an overview
No full textThe paper presents an overview of global issues in optimizationmethods for training feedforward
neural networks (FNN) in a regression setting.We first recall the learning optimization
paradigm for FNN and we briefly discuss global scheme for the joint choice of the network
topologies and of the network parameters. The main part of the paper focuses on the
core subproblem which is the continuous unconstrained (regularized) weights optimization
problem with the aim of reviewing global methods specifically arising both in multi layer
perceptron/deep networks and in radial basis networks.We review some recent results on the
existence of non-global stationary points of the unconstrained nonlinear problem and the role
of determining a global solution in a supervised learning paradigm. Local algorithms that are
widespread used to solve the continuous unconstrained problems are addressed with focus on
possible improvements to exploit the global properties. Hybrid global methods specifically
devised for FNN training optimization problems which embed local algorithms are discussed
too
Global Optimization issues in Supervised Learning An overview
No full textThe paper presents an overview of global issues in optimization
methods for Supervised Learning (SL). We focus on Feedforward Neural Networks with the aim of reviewing global methods specifically devised for the
class of continuous unconstrained optimization problems arising both in MultiLayer Perceptron/Deep Networks and in Radial Basis Networks. We first recall the learning optimization paradigm for FNN and we briefly discuss global
scheme for the joined choice of the network topologies and of the network parameters. The main part of the paper focus on the core subproblem which is the
unconstrained regularized weight optimization problem. We review some recent results on the existence of local-non global solutions of the unconstrained
nonlinear problem and the role of determining a global solution in a Machine
Learning paradigm. Local algorithms that are widespread used to solve the
continuous unconstrained problems are addressed with focus on possible improvements to exploit the global properties. Hybrid global methods specifically
devised for SL optimization problems which embed local algorithms are discussed at the end
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