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Meta-learning / Meta-learningHovorka, Martin January 2008 (has links)
Goal of this work is to make acquaintance and study meta-learningu methods, program algorithm and compare with other machine learning methods.
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A Statistical Analysis of Medical Data for Breast Cancer and Chronic Kidney DiseaseYang, Kaolee 05 May 2020 (has links)
No description available.
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Regularization: Stagewise Regression and BaggingEhrlinger, John M. 31 March 2011 (has links)
No description available.
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OPTIMIZING DECISION TREE ENSEMBLES FOR GENE-GENE INTERACTION DETECTIONAssareh, Amin 27 November 2012 (has links)
No description available.
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Influence of bagging materials on maturity indices and post-harvest quality of 'roma vf' and 'tinker' cherry tomatoesMafotja, Mokgadi Pollet January 2022 (has links)
Thesis (M.Sc. (Horticulture)) -- University of Limpopo, 2022 / The purpose of this study was to assess the impact of pre-harvest bagging materials
on maturity indices and post-harvest quality of cherry tomatoes. At pre-harvest, fruit
were bagged with blue and transparent plastic bags of 0.075- and 0.025-mm
thickness, respectively. The non-bagged fruit were considered as control treatment.
The experiment was carried out in a randomized complete block design arranged in
a 2 × 3 factorial arrangement with three replications. Physical quality parameters
such as; colour changes (L*, a*, b*, C*, h˚, and ΔE), firmness, weight loss and size
were assessed. Physico-chemical parameters such as pH, total soluble solids, and
total titratable acidity were also evaluated. Bagging had a significant effect on the
quality of both cherry tomato cultivars. The results showed that bagging cherry
tomatoes at 1.5 cm diameter with blue and transparent plastic bags accelerated
maturity. Moreover, bagging with transparent plastic bags enhanced exocarp colour,
reduced weight loss, retained larger size, increased pH and TTA, with an increase in
TSS when compared with blue plastic bags and control, respectively at 12 days of
shelf-life. In conclusion, the findings demonstrate that pre-harvest bagging has the
potential to improve maturity indices and post-harvest quality of cherry tomatoes.
Therefore, pre-harvest bagging can be used as an alternative method to enhance
cherry tomato fruit quality and shelf-life. / NRF
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Forêts Aléatoires: De l'Analyse des Mécanismes de Fonctionnement à la Construction DynamiqueBernard, Simon 02 December 2009 (has links) (PDF)
Les travaux de cette thèse se situent dans le domaine de l'apprentissage automatique et concernent plus particulièrement la paramétrisation des forêts aléatoires, une technique d'ensembles de classifieurs utilisant des arbres de décision. Nous nous intéressons à deux paramètres importants pour l'induction de ces forêts: le nombre de caractéristiques choisies aléatoirement à chaque noeud et le nombre d'arbres. Nous montrons d'abord que la valeur du premier paramètre doit être choisie en fonction des propriétés de l'espace de description, et proposons dans ce cadre un nouvel algorithme nommé Forest-RK exploitant ces propriétés. Nous montrons ensuite qu'avec un processus statique d'induction de Forêts, certains arbres provoquent une diminution des performances de l'ensemble, en dégradant le compromis force/ corrélation. Nous en déduisons un algorithme d'induction dynamique particulièrement performant en comparaison avec les procédures d'induction statique.
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Bootstrap bandwidth selection in kernel hazard rate estimation / S. Jansen van VuurenVan Vuuren, Stefan Jansen January 2011 (has links)
The purpose of this study is to thoroughly discuss kernel hazard function estimation, both
in the complete sample case as well as in the presence of random right censoring. Most of
the focus is on the very important task of automatic bandwidth selection. Two existing
selectors, least–squares cross validation as described by Patil (1993a) and Patil (1993b), as
well as the bootstrap bandwidth selector of Gonzalez–Manteiga, Cao and Marron (1996) will
be discussed. The bandwidth selector of Hall and Robinson (2009), which uses bootstrap
aggregation (or 'bagging'), will be extended to and evaluated in the setting of kernel hazard
rate estimation. We will also make a simple proposal for a bootstrap bandwidth selector.
The performance of these bandwidth selectors will be compared empirically in a simulation
study. The findings and conclusions of this study are reported. / Thesis (M.Sc. (Statistics))--North-West University, Potchefstroom Campus, 2011.
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Bootstrap bandwidth selection in kernel hazard rate estimation / S. Jansen van VuurenVan Vuuren, Stefan Jansen January 2011 (has links)
The purpose of this study is to thoroughly discuss kernel hazard function estimation, both
in the complete sample case as well as in the presence of random right censoring. Most of
the focus is on the very important task of automatic bandwidth selection. Two existing
selectors, least–squares cross validation as described by Patil (1993a) and Patil (1993b), as
well as the bootstrap bandwidth selector of Gonzalez–Manteiga, Cao and Marron (1996) will
be discussed. The bandwidth selector of Hall and Robinson (2009), which uses bootstrap
aggregation (or 'bagging'), will be extended to and evaluated in the setting of kernel hazard
rate estimation. We will also make a simple proposal for a bootstrap bandwidth selector.
The performance of these bandwidth selectors will be compared empirically in a simulation
study. The findings and conclusions of this study are reported. / Thesis (M.Sc. (Statistics))--North-West University, Potchefstroom Campus, 2011.
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Novas abordagens para configura??es autom?ticas dos par?metros de controle em comit?s de classificadoresNascimento, Diego Silveira Costa 05 December 2014 (has links)
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Previous issue date: 2014-12-05 / Significativos avan?os v?m surgindo em pesquisas relacionadas ao tema de Comit?s de Classificadores.
Os modelos que mais recebem aten??o na literatura s?o aqueles de natureza est?tica,
ou tamb?m conhecidos por ensembles. Dos algoritmos que fazem parte dessa classe, destacam-se
os m?todos que utilizam reamostragem dos dados de treinamento: Bagging, Boosting e Multiboosting.
A escolha do tipo de arquitetura e dos componentes a serem recrutados n?o ? uma tarefa
trivial, e tem motivado, ainda mais, o surgimento de novas propostas na tentativa de se construir
tais modelos de forma autom?tica e, muitas delas, s?o baseadas em m?todos de otimiza??o.
Muitas dessas contribui??es n?o t?m apresentado resultados satisfat?rios quando aplicadas a
problemas mais complexos ou de natureza distinta. Em contrapartida, a tese aqui apresentada
prop?e tr?s novas abordagens h?bridas para constru??o autom?tica em ensembles de classificadores:
Incremento de Diversidade, Fun??o de Avalia??o Adaptativa e Meta-aprendizado para a
elabora??o de sistemas de configura??o autom?tica dos par?metros de controle para os modelos
de ensemble. Na primeira abordagem, ? proposta uma solu??o que combina diferentes t?cnicas
de diversidade em um ?nico arcabou?o conceitual, na tentativa de se alcan?ar n?veis mais elevados
de diversidade em ensemble, e com isso, melhor o desempenho de tais sistemas. J? na
segunda abordagem, ? utilizado um algoritmo gen?tico para o design autom?tico de ensembles.
A contribui??o consiste em combinar as t?cnicas de filtro e wrapper de forma adaptativa para
evoluir uma melhor distribui??o do espa?o de atributos a serem apresentados aos componentes
de um ensemble. E por fim, a ?ltima abordagem, que prop?e uma nova t?cnica de recomenda??o
de arquitetura e componentes base em ensemble, via t?cnicas de meta-aprendizado tradicional e
multirr?tulo. De forma geral os resultados s?o animadores, e corroboram com a tese de que ferramentas
h?bridas s?o uma poderosa solu??o na constru??o de ensembles eficazes em problemas
de classifica??o de padr?es / Significant advances have emerged in research related to the topic of Classifier Committees.
The models that receive the most attention in the literature are those of the static nature, also
known as ensembles. The algorithms that are part of this class, we highlight the methods that
using techniques of resampling of the training data: Bagging, Boosting and Multiboosting. The
choice of the architecture and base components to be recruited is not a trivial task and has motivated
new proposals in an attempt to build such models automatically, and many of them are
based on optimization methods. Many of these contributions have not shown satisfactory results
when applied to more complex problems with different nature. In contrast, the thesis presented
here, proposes three new hybrid approaches for automatic construction for ensembles: Increment
of Diversity, Adaptive-fitness Function and Meta-learning for the development of systems
for automatic configuration of parameters for models of ensemble. In the first one approach, we
propose a solution that combines different diversity techniques in a single conceptual framework,
in attempt to achieve higher levels of diversity in ensembles, and with it, the better the performance
of such systems. In the second one approach, using a genetic algorithm for automatic
design of ensembles. The contribution is to combine the techniques of filter and wrapper adaptively
to evolve a better distribution of the feature space to be presented for the components of
ensemble. Finally, the last one approach, which proposes new techniques for recommendation of
architecture and based components on ensemble, by techniques of traditional meta-learning and
multi-label meta-learning. In general, the results are encouraging and corroborate with the thesis
that hybrid tools are a powerful solution in building effective ensembles for pattern classification
problems.
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Investigation of multivariate prediction methods for the analysis of biomarker dataHennerdal, Aron January 2006 (has links)
The paper describes predictive modelling of biomarker data stemming from patients suffering from multiple sclerosis. Improvements of multivariate analyses of the data are investigated with the goal of increasing the capability to assign samples to correct subgroups from the data alone. The effects of different preceding scalings of the data are investigated and combinations of multivariate modelling methods and variable selection methods are evaluated. Attempts at merging the predictive capabilities of the method combinations through voting-procedures are made. A technique for improving the result of PLS-modelling, called bagging, is evaluated. The best methods of multivariate analysis of the ones tried are found to be Partial least squares (PLS) and Support vector machines (SVM). It is concluded that the scaling have little effect on the prediction performance for most methods. The method combinations have interesting properties – the default variable selections of the multivariate methods are not always the best. Bagging improves performance, but at a high cost. No reasons for drastically changing the work flows of the biomarker data analysis are found, but slight improvements are possible. Further research is needed.
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