Search results for "generalization"
showing 10 items of 250 documents
The Local Fractional Derivative of Fractal Curves
2008
Fractal curves described by iterated function system (IFS) are generally non-integer derivative. For that we use fractional derivative to investigate differentiability of this curves. We propose a method to calculate local fractional derivative of a curve from IFS property. Also we give some examples of IFS representing the slopes of the right and left half-tangent of the fractal curves.
Learning by the Process of Elimination
2002
AbstractElimination of potential hypotheses is a fundamental component of many learning processes. In order to understand the nature of elimination, herein we study the following model of learning recursive functions from examples. On any target function, the learning machine has to eliminate all, save one, possible hypotheses such that the missing one correctly describes the target function. It turns out that this type of learning by the process of elimination (elm-learning, for short) can be stronger, weaker or of the same power as usual Gold style learning.While for usual learning any r.e. class of recursive functions can be learned in all of its numberings, this is no longer true for el…
The absolute center of a unicyclic network
1989
Abstract A unicyclic network is one generalization of a tree network. In this paper we examine the problem of finding an absolute center of a unicyclic network. We show that this problem can be solved in linear time with respect to the number of vertices in the network.
Multiscale Granger causality analysis by à trous wavelet transform
2017
Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling: to overcome these problems, we propose a wavelet-based approach for multiscale Granger causality (GC) analysis, which is characterized by the following properties: (i) only the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the…
Multi-agent Reinforcement Learning for Simulating Pedestrian Navigation
2012
In this paper we introduce a Multi-agent system that uses Reinforcement Learning (RL) techniques to learn local navigational behaviors to simulate virtual pedestrian groups. The aim of the paper is to study empirically the validity of RL to learn agent-based navigation controllers and their transfer capabilities when they are used in simulation environments with a higher number of agents than in the learned scenario. Two RL algorithms which use Vector Quantization (VQ) as the generalization method for the space state are presented. Both strategies are focused on obtaining a good vector quantizier that generalizes adequately the state space of the agents. We empirically state the convergence…
Improving estimation of distribution genetic programming with novelty initialization
2021
Estimation of distribution genetic programming (EDA-GP) replaces the standard variation operations of genetic programming (GP) by learning and sampling from a probabilistic model. Unfortunately, many EDA-GP approaches suffer from a rapidly decreasing population diversity which often leads to premature convergence. However, novelty search, an approach that searches for novel solutions to cover sparse areas of the search space, can be used for generating diverse initial populations. In this work, we propose novelty initialization and test this new method on a generalization of the royal tree problem and compare its performance to ramped half-and-half (RHH) using a recent EDA-GP approach. We f…
La enseñanza basada en preguntas : La ley de Ampère y el término de Maxwell
2020
Frecuentemente a los maestros de Física y de Matemáticas se les recomienda una enseñanza activa de estas áreas del conocimiento, que consiste en una interacción continua entre el maestro y el estudiante. Con una metáfora de un interrogatorio adecuado, se pretende mostrar al docente una manera de orientar al estudiante en su aprendizaje, motivándolo a recuperar conocimientos previos o causándole un conflicto cognitivo que lo lleve a reformular su aprendizaje. Esta manera de proceder didácticamente se ha llamado enseñanza basada en preguntas, cuya primera referencia histórica nos remite a la Grecia antigua. En este trabajo se presenta la enseñanza de la ley de Ampère con la generalización de …
Representation of Strongly Stationary Stochastic Processes
1993
A generalization of the orthogonality conditions for a stochastic process to represent strongly stationary processes up to a fixed order is presented. The particular case of non-normal delta correlated processes, and the probabilistic characterization of linear systems subjected to strongly stationary stochastic processes are also discussed.
Enclosure method for the p-Laplace equation
2014
We study the enclosure method for the p-Calder\'on problem, which is a nonlinear generalization of the inverse conductivity problem due to Calder\'on that involves the p-Laplace equation. The method allows one to reconstruct the convex hull of an inclusion in the nonlinear model by using exponentially growing solutions introduced by Wolff. We justify this method for the penetrable obstacle case, where the inclusion is modelled as a jump in the conductivity. The result is based on a monotonicity inequality and the properties of the Wolff solutions.
Artificial neural networks for predicting dorsal pressures on the foot surface while walking
2012
In this work, artificial neural networks (ANNs) are proposed to predict the dorsal pressure over the foot surface exerted by the shoe upper while walking. A model that is based on the multilayer perceptron (MLP) is used since it can provide a single equation to model the exerted pressure for all the materials used as shoe uppers. Five different models are produced, one model for each one of the four subjects under study and an overall model for the four subjects. The inputs to the neural model include the characteristics of the material and the positions during a whole step of 14 pressure sensors placed on the foot surface. The goal is to find models with good generalization capabilities, (…