8 years 4 months ago
Greedy geographic routing in large-scale sensor networks: a minimum network decomposition approach
In geographic (or geometric) routing, messages are expected to route in a greedy manner: the current node always forwards a message to its neighbor node that is closest to the des...
Anne-Marie Kermarrec, Guang Tan
158views more  IJCV 2010»
8 years 4 months ago
Metric Learning for Image Alignment
Abstract Image alignment has been a long standing problem in computer vision. Parameterized Appearance Models (PAMs) such as the Lucas-Kanade method, Eigentracking, and Active Appe...
Minh Hoai Nguyen, Fernando De la Torre
126views more  TIP 2002»
8 years 5 months ago
Template matching based object recognition with unknown geometric parameters
In this paper, we examine the problem of locating an object in an image when size and rotation are unknown. Previous work has shown that with known geometric parameters, an image r...
Roger M. Dufour, Eric L. Miller, Nikolas P. Galats...
99views more  TNN 1998»
8 years 6 months ago
Comments on local minima free conditions in multilayer perceptrons
—In this letter we point out that multilayer neural networks (MLP’s) with either sigmoidal units or radial basis functions can be given a canonical form with positive interunit...
Marco Gori, Ah Chung Tsoi
123views Neural Networks» more  NN 2000»
8 years 6 months ago
Local minima and plateaus in hierarchical structures of multilayer perceptrons
Local minima and plateaus pose a serious problem in learning of neural networks. We investigate the hierarchical geometric structure of the parameter space of three-layer perceptr...
Kenji Fukumizu, Shun-ichi Amari
123views more  JCO 2007»
8 years 6 months ago
On the number of local minima for the multidimensional assignment problem
The Multidimensional Assignment Problem (MAP) is an NP-hard combinatorial optimization problem occurring in many applications, such as data association, target tracking, and resou...
Don A. Grundel, Pavlo A. Krokhmal, Carlos A. S. Ol...
81views more  MOR 2006»
8 years 6 months ago
Simulated Annealing for Convex Optimization
We apply the method known as simulated annealing to the following problem in convex optimization: minimize a linear function over an arbitrary convex set, where the convex set is ...
Adam Tauman Kalai, Santosh Vempala
94views Education» more  CORR 2007»
8 years 6 months ago
Statistical tools to assess the reliability of self-organizing maps
Results of neural network learning are always subject to some variability, due to the sensitivity to initial conditions, to convergence to local minima, and, sometimes more dramat...
Eric de Bodt, Marie Cottrell, Michel Verleysen
102views more  ISCI 2008»
8 years 6 months ago
Random walk biclustering for microarray data
A biclustering algorithm, based on a greedy technique and enriched with a local search strategy to escape poor local minima, is proposed. The algorithm starts with an initial rand...
Fabrizio Angiulli, Eugenio Cesario, Clara Pizzuti
182views more  CPHYSICS 2006»
8 years 6 months ago
MinFinder: Locating all the local minima of a function
A new stochastic clustering algorithm is introduced that aims to locate all the local minima of a multidimensional continuous and differentiable function inside a bounded domain. ...
Ioannis G. Tsoulos, Isaac E. Lagaris