Feria Infectologia
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La Facultad de Medicina de la Universidad Autónoma Gabriel René Moreno organizó una feria de enfermedades infecciosas el 20 de diciembre de 2003 en la Plaza 24 de septiembre en Santa Cruz de la Sierra.
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Let's get ready to rumble redux: Crossover versus mutation head to head on ex...
This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of non-uniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentially-scaled additively separable, problems, the BB-wise mutation is more efficient than crossover yielding a speedup of Θ(l logl), where l is the problem size. For the noisy exponentially-scaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of Θ(l logl). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of Θ(l).
Substructrual surrogates for learning decomposable classification problems: i...
This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model that represents salient interactions between attributes for a given data, (2) a surrogate model which provides a functional approximation of the output as a function of attributes, and (3) a classification model which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate and its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects the interacting variables in hierarchical problems, group them in linkages groups, and build maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and have also shed light on several improvements to enhance the capabilities of the proposed method.
PresentacióN Medula Espinal
Trabajo práctico en Power Ponit Médula Espinal. Catedra: Infrmatica I. Coisión: 1ro C mañana.
Feria Infectologia
Feria de Enfermedades Infecciosas, realizada en la Plaza principal de Santa Cruz el 23 de diciembre del 2003. UAGRM, carrera de Medicina
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Let's get ready to rumble redux: Crossover versus mutation head to head on ex...
This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of non-uniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentially-scaled additively separable, problems, the BB-wise mutation is more efficient than crossover yielding a speedup of Θ(l logl), where l is the problem size. For the noisy exponentially-scaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of Θ(l logl). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of Θ(l).
Substructrual surrogates for learning decomposable classification problems: i...
This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model that represents salient interactions between attributes for a given data, (2) a surrogate model which provides a functional approximation of the output as a function of attributes, and (3) a classification model which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate and its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects the interacting variables in hierarchical problems, group them in linkages groups, and build maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and have also shed light on several improvements to enhance the capabilities of the proposed method.
PresentacióN Medula Espinal
Trabajo práctico en Power Ponit Médula Espinal. Catedra: Infrmatica I. Coisión: 1ro C mañana.
Feria Infectologia
Feria de Enfermedades Infecciosas, realizada en la Plaza principal de Santa Cruz el 23 de diciembre del 2003. UAGRM, carrera de Medicina
Modeling selection pressure in XCS for proportionate and tournament selection
In this paper, we derive models of the selection pressure in XCS for proportionate (roulette wheel) selection and tournament selection. We show that these models can explain the empirical results that have been previously presented in the literature. We validate the models on simple problems showing that, (i) when the model assumptions hold, the theory perfectly matches the empirical evidence; (ii) when the model assumptions do not hold, the theory can still provide qualitative explanations of the experimental results.
Feria de Enfermedades Infecciosas
Feria de Enfermedades Infecciosas, realizada en la plaza principal de Santa Cruz de la Sierra en diciembre del 2003. UAGRM, carrera de Medicina
PresentacióN Medula Espinal
Trabajo práctico Power Point "Médula espinal" Catedra: Informatica I. Comisión: 1ro C mañana. Facultad de medicina y ciencias de la salud.
Saludo a María
Cortesía Salesianos Bilbao - Inspectoría San Francisco Javier - http://www.salesianobilbao.com
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Modeling selection pressure in XCS for proportionate and tournament selection
In this paper, we derive models of the selection pressure in XCS for proportionate (roulette wheel) selection and tournament selection. We show that these models can explain the empirical results that have been previously presented in the literature. We validate the models on simple problems showing that, (i) when the model assumptions hold, the theory perfectly matches the empirical evidence; (ii) when the model assumptions do not hold, the theory can still provide qualitative explanations of the experimental results.
Feria de Enfermedades Infecciosas
Feria de Enfermedades Infecciosas, realizada en la plaza principal de Santa Cruz de la Sierra en diciembre del 2003. UAGRM, carrera de Medicina
PresentacióN Medula Espinal
Trabajo práctico Power Point "Médula espinal" Catedra: Informatica I. Comisión: 1ro C mañana. Facultad de medicina y ciencias de la salud.
Saludo a María
Cortesía Salesianos Bilbao - Inspectoría San Francisco Javier - http://www.salesianobilbao.com
Destacado (20)
Modeling selection pressure in XCS for proportionate and tournament selection
Modeling selection pressure in XCS for proportionate and tournament selection
Feria Infectologia
- 1. FERIA DE ENFERMEDADES INFECCIOSAS CARRERA DE MEDICINA DE LA UAGRM DIC 20, 2003 Plaza 24 de septiembre, Santa Cruz de la Sierra.