Recursive Partitioning
Mostrando 1-12 de 13 artigos, teses e dissertações.
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1. MODELOS NEURO-FUZZY HIERÁRQUICOS BSP DO TIPO 2 / TYPE-2 HIERARCHICAL NEURO-FUZZY BSP MODEL
The objective of this thesis is to create a new type-2 fuzzy inference system for the treatment of uncertainties with automatic learning and that provides an interval of confidence for its defuzzified output through the calculation of corresponding type-reduced sets. In order to attain this objective, this new model combines the paradigms of the modelling of
Publicado em: 2007
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2. APLICAÇÃO DE MODELOS NÃO LINEARES EM NEGOCIAÇÃO AUTOMÁTICA NO MERCADO ACIONÁRIO BRASILEIRO / APPLICATION OF NONLINEAR MODELS FOR AUTOMATIC TRADING IN THE BRAZILIAN STOCK MARKET
Esta dissertação tem por objetivo comparar o desempenho de modelos não lineares de previsão de retornos em 10 ativos do mercado acionário brasileiro. Entre os modelos escolhidos, pode-se citar o STAR-Tree, que combina conceitos da metodologia STAR (Smooth Transition AutoRegression) e do algoritmo CART (Classification And Regression Trees), tendo como re
Publicado em: 2006
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3. TREE-STRUCTURED SMOOTH TRANSITION REGRESSION MODELS / MODELOS DE REGRESSÃO COM TRANSIÇÃO SUAVE ESTRUTURADOS POR ÁRVORES
O objetivo principal desta tese introduzir um modelo estruturado por árvores que combina aspectos de duas metodologias: CART (Classification and Regression Tree) e STR (Smooth Transition Regression). O modelo aqui denominado STR-Tree. A idéia especificar um modelo não-linear paramétrico através da estrutura de uma árvore de decisão binária. O modelo
Publicado em: 2005
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4. HIERARQUICAL NEURO-FUZZY MODELS BASED ON REINFORCEMENT LEARNING FOR INTELLIGENT AGENTS / NOVOS MODELOS NEURO-FUZZY HIERÁRQUICOS COM APRENDIZADO POR REFORÇO PARA AGENTES INTELIGENTES
This thesis investigates neuro-fuzzy hybrid models for automatic learning of actions taken by agents. The objective of these models is to provide an agent with intelligence, making it capable of acquiring and retaining knowledge and of reasoning (infer an action) by interacting with its environment. Learning in these models is performed by a non-supervised p
Publicado em: 2003
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5. NEURO-FUZZY BSP HIERARCHICAL SYSTEM FOR TIME FORECASTING AND FUZZY RULE EXTRACTION DOR DATA MINING APPLICATONS / SISTEMA NEURO-FUZZY HIERÁRQUICO BSP PARA PREVISÃO E EXTRAÇÃO DE REGRAS FUZZY EM APLICAÇÕES DE DATA MINING
This dissertation investigates the use of a Neuro-Fuzzy Hierarchical system for time series forecasting and fuzzy rule extraction for Data Mining applications. The objective of this work was to extend the Neuro-Fuzzy BSP Hierarchical model for the classification of registers and time series forecasting. The process of classification of registers in the Data
Publicado em: 2000
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6. HIERARCHICAL NEURO-FUZZY MODELS / MODELOS NEURO-FUZZY HIERÁRQUICOS
This dissertation presents a new proposal of neurofuzzy systems (models), which present, in addition to the learning capacity (which are common to the neural networks and neurofuzzy systems) the following features: learning of the structure; the use of recursive partitioning; a greater number of inputs than usually allowed in neurofuzzy systems; and hierarch
Publicado em: 1999
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7. Genomewide Scan of Hoarding in Sib Pairs in Which Both Sibs Have Gilles de la Tourette Syndrome
A genome scan of the hoarding phenotype (a component of obsessive-compulsive disorder) was conducted on 77 sib pairs collected by the Tourette Syndrome Association International Consortium for Genetics (TSAICG). All sib pairs were concordant for a diagnosis of Gilles de la Tourette syndrome (GTS). However, the analyses reported here were conducted for hoardi
The American Society of Human Genetics.
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8. Recursive partitioning for tumor classification with gene expression microarray data
Precise classification of tumors is critically important for cancer diagnosis and treatment. It is also a scientifically challenging task. Recently, efforts have been made to use gene expression profiles to improve the precision of classification, with limited success. Using a published data set for purposes of comparison, we introduce a methodology bas
The National Academy of Sciences.
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9. Cell and tumor classification using gene expression data: Construction of forests
The advent of gene chips has led to a promising technology for cell, tumor, and cancer classification. We exploit and expand the methodology of recursive partitioning trees for tumor and cell classification from microarray gene expression data. To improve classification and prediction accuracy, we introduce a deterministic procedure to form forests of classi
The National Academy of Sciences.
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10. Case-mix adjustment using objective measures of severity: the case for laboratory data.
OBJECTIVE. We evaluate the use of routinely gathered laboratory data to subclassify surgical and nonsurgical major diagnostic categories into groups homogeneous with respect to length of stay (LOS). DATA SOURCES AND STUDY SETTING. The source of data is the Combined Patient Experience database (COPE), created by merging data from computerized sources at the U
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11. The simultaneous evolution of author and paper networks
There has been a long history of research into the structure and evolution of mankind's scientific endeavor. However, recent progress in applying the tools of science to understand science itself has been unprecedented because only recently has there been access to high-volume and high-quality data sets of scientific output (e.g., publications, patents, gran
National Academy of Sciences.
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12. Development of function-related groups version 2.0: a classification system for medical rehabilitation.
OBJECTIVE: To present a new version (2.0) of the Functional Independence Measure-Function Related Group (FIM-FRG) case-mix measure. DATA SOURCE/STUDY SETTING: 85,447 patient discharges from 252 freestanding facilities and hospital units contained in the 1992 Uniform Data System for Medical Rehabilitation. STUDY DESIGN: Patient impairment category, functional