Symbolic Machine Learning
Mostrando 1-9 de 9 artigos, teses e dissertações.
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1. Uma metodologia para extração de conhecimento em séries temporais por meio da identificação de motifs e da extração de características / A methodology to extract knowledge from time series using motif identification and feature extraction
Data mining has been applied to several areas with the objective of extracting interesting and relevant knowledge from large data bases. In this scenario, machine learning provides some of the main methods employed in data mining. Symbolic learning are among the most used machine learning methods since these methods can provide models that can be interpreted
Publicado em: 2009
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2. Computação Evolutiva para a Construção de Regras de Conhecimento com Propriedades Específicas / Evolutionary Computing for Knowledge Rule Construction with Specific Properties
Most symbolic machine learning approaches use if-then know-ledge rules as the description language in which the learned knowledge is expressed. The aim of these learners is to find a set of classification rules that can be used to predict new instances that have not been seen by the learner before. However, these sorts of learners take into account the rule
Publicado em: 2007
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3. Abordagem simbólica de aprendizado de máquina na recuperação automática de artigos científicos a partir de web / Symbolic approach of machine learning in the scientific article automatic recovery from the web
Atualmente, devido ao incessante aumento dos documentos científicos disponíveis na rede mundial de computadores, as ferrametas de busca tornaram-se um importante auxílio para recuperação de informação a partir da Internet em todas as áreas de conhecimento para pesquisadores e usuários. Entretanto, as atuais ferramentas de busca disponíveis selecion
Publicado em: 2006
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4. Dissimilarity fuctions analysis based on dynamic clustering for symbolic data
Symbolic Data Analysis (SDA) is a new domain in the area of knowledge discovery that aims to provide suitable methods for data described through multi-valued variables, where there are sets of categories, intervals, or weight (probability) distributions in the cells of the data tables. These new variables enable representing the variability and uncertainty p
Publicado em: 2005
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5. Looking for exceptions on knowledge rules induced from HIV cleavage data set
The aim of data mining is to find useful knowledge inout of databases. In order to extract such knowledge, several methods can be used, among them machine learning (ML) algorithms. In this work we focus on ML algorithms that express the extracted knowledge in a symbolic form, such as rules. This representation may allow us to ''explain'' the data. Rule learn
Genetics and Molecular Biology. Publicado em: 2004
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6. Extraction of knowledge from Artificial Neural Networks using Symbolic Machine Learning Systems and Genetic Algorithm / "Extração de conhecimento de redes neurais artificiais utilizando sistemas de aprendizado simbólico e algoritmos genéticos"
In Machine Learning - ML there is not a single algorithm that is the best for all application domains. In practice, several research works have shown that Artificial Neural Networks - ANNs have an appropriate inductive bias for several domains. Thus, ANNs have been applied to a number of data sets with high predictive accuracy. Symbolic ML algorithms have a
Publicado em: 2003
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7. Combination of symbolic classifiers to improve predictive and descriptive power of ensembles / "Combinação de classificadores simbólicos para melhorar o poder preditivo e descritivo de Ensembles"
A qualidade das hipóteses induzidas pelos atuais sistemas de Aprendizado de Máquina depende principalmente da quantidade e da qualidade dos atributos e exemplos utilizados no treinamento. Freqüentemente, resultados experimentais obtidos sobre grandes bases de dados, que possuem muitos atributos irrelevantes, resultam em hipóteses de baixa precisão. Por
Publicado em: 2002
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8. Um ambiente para avaliação de algoritmos de aprendizado de máquina simbólico utilizando exemplos. / An environment to evaluate machine learning algorithms.
A learning system is a computer program that makes decisions based on the accumulative experience contained in successfully solved cases. The classification rules induced by a learning system are judged by two criteria: their classification error on an independent test set and their complexity. Practical learning systems have been developed using different p
Publicado em: 1997
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9. A dynamical systems perspective on the relationship between symbolic and non-symbolic computation
It has been claimed that connectionist (artificial neural network) models of language processing, which do not appear to employ “rules”, are doing something different in kind from classical symbol processing models, which treat “rules” as atoms (e.g., McClelland and Patterson in Trends Cogn Sci 6(11):465–472, 2002). This claim is hard to assess in
Springer Netherlands.