Machine Learning Classifiers
Mostrando 1-12 de 30 artigos, teses e dissertações.
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1. Breast Cancer Prediction Using Dominance-based Feature Filtering Approach: A Comparative Investigation in Machine Learning Archetype
Abstract Breast cancer is the most commonly witnessed cancer amongst women around the world. Computer aided diagnosis (CAD) have been playing a significant role in early detection of breast tumors hence to curb the overall mortality rate. This work presents an enhanced empirical study of impact of dominance-based filtering approach on performances of variou
Braz. arch. biol. technol.. Publicado em: 25/11/2019
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2. Preprocessing procedures and supervised classification applied to a database of systematic soil survey
ABSTRACT: Data Mining techniques play an important role in the prediction of soil spatial distribution in systematic soil surveying, though existing methodologies still lack standardization and a full understanding of their capabilities. The aim of this work was to evaluate the performance of preprocessing procedures and supervised classification approaches
Sci. agric. (Piracicaba, Braz.). Publicado em: 20/05/2019
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3. Development of a skateboarding trick classifier using accelerometry and machine learning
Abstract Introduction Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement
Res. Biomed. Eng.. Publicado em: 2017-10
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4. An Empirical Evaluation of the Local Texture Description Framework-Based Modified Local Directional Number Pattern with Various Classifiers for Face Recognition
ABSTRACT Texture is one of the chief characteristics of an image. In recent years, local texture descriptors have garnered attention among researchers in describing effective texture patterns to demarcate facial images. A feature descriptor titled Local Texture Description Framework-based Modified Local Directional Number pattern (LTDF_MLDN), capable of enco
Braz. arch. biol. technol.. Publicado em: 23/01/2017
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5. Seleção de componentes em ensembles de clasificadores multirrótulo / Component Selection in Ensembles of Multi-label Classifiers
The selection of components in ensembles of classifiers is a very common activity in the field of Machine Learning with several studies showing its effectiveness in obtaining significant gains in accuracy. However, the most studied classification task involves mutually exclusive labels (classes). The objective of this work is to present a study on the select
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 27/07/2012
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6. Algoritmo de aprendizado supervisionado - baseado em máquinas de vetores de suporte - uma contribuição para o reconhecimento de dados desbalanceados / Supervised learning Algorithm - Based on Support Vector Machines - A Contribution to the Recognition of Unbalanced Data
The machine learning in datasets that have unbalanced classes, has received considerable attention in the scientific community, because the traditional classification algorithms dont provide a satisfactory performance. This low performance can be explained by the fact that the traditional techniques of machine learning consider that each class present in the
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 26/09/2011
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7. Ensembles na classificação relacional / Ensembles in relational classification
Em diversos domínios, além das informações sobre os objetos ou entidades que os compõem, existem, também, informaçõoes a respeito das relações entre esses objetos. Alguns desses domínios são, por exemplo, as redes de co-autoria, e as páginas Web. Nesse sentido, é natural procurar por técnicas de classificação que levem em conta estas informa
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 08/09/2011
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8. Aplicação de modelos de Markov ocultos na obtenção de taxas de mortalidade das larvas do mosquito da Dengue
In order to prevent the proliferation of Dengue transmitter - scientifically named as Aedes ae- gypti - and therefore decrease human contamination by such insect, many larvaecides have been developed recently. Researchers from Dom Bosco Catholic University evaluate the efectiveness of vegetal-derived substances capable to combat such animal larvae. Death rat
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 26/02/2010
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9. Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinations
PURPOSE: To evaluate the performance of support vector machine, multi-layer perceptron and radial basis function neural network as auxiliary tools to identify keratoconus from Orbscan II maps. METHODS: A total of 318 maps were selected and classified into four categories: normal (n = 172), astigmatism (n = 89), keratoconus (n = 46) and photorefractive kerate
Clinics. Publicado em: 2010
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10. Semi-supervised learning based in disagreement by similarity / Classificação semi-supervisionada baseada em desacordo por similaridade
Semi-supervised learning is a machine learning paradigm in which the induced hypothesis is improved by taking advantage of unlabeled data. Semi-supervised learning is particularly useful when labeled data is scarce and difficult to obtain. In this context, the Cotraining algorithm was proposed. Cotraining is a widely used semisupervised approach that assumes
Publicado em: 2010
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11. APLICAÇÃO DE TÉCNICAS DE APRENDIZADO DE MÁQUINA PARA CLASSIFICAÇÃO DE DEPÓSITOS MINERAIS BASEADA EM MODELO TEOR-TONELAGEM / APPLICATION OF MACHINE LEARNING TECHNIQUES FOR CLASSIFICATION OF MINERAL DEPOSITS CONTENT-BASED MODEL TONNAGE
Classification of mineral deposits into types is traditionally done by experts. Since there are reasons to believe that computational techniques can aid this classification process and make it less subjective, the research and investigation of different methods of clustering and classification to this domain may be appropriate. The way followed by researches
Publicado em: 2010
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12. Pre-processing for noise detection in gene expression classification data
Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce nois
Journal of the Brazilian Computer Society. Publicado em: 2009-03