21 research outputs found
On structure, family and parameter estimation of hierarchical Archimedean copulas
Research on structure determination and parameter estimation of hierarchical
Archimedean copulas (HACs) has so far mostly focused on the case in which all
appearing Archimedean copulas belong to the same Archimedean family. The
present work addresses this issue and proposes a new approach for estimating
HACs that involve different Archimedean families. It is based on employing
goodness-of-fit test statistics directly into HAC estimation. The approach is
summarized in a simple algorithm, its theoretical justification is given and
its applicability is illustrated by several experiments, which include
estimation of HACs involving up to five different Archimedean families.Comment: 63 pages, one attachment in attachment.pd
Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context
Surrogate modeling has become a valuable technique for black-box optimization
tasks with expensive evaluation of the objective function. In this paper, we
investigate the relationship between the predictive accuracy of surrogate
models and features of the black-box function landscape. We also study
properties of features for landscape analysis in the context of different
transformations and ways of selecting the input data. We perform the landscape
analysis of a large set of data generated using runs of a surrogate-assisted
version of the Covariance Matrix Adaptation Evolution Strategy on the noiseless
part of the Comparing Continuous Optimisers benchmark function testbed.Comment: 25 pages main article, 28 pages supplementary material, 3 figures,
currently under review at Evolutionary Computation journa
Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts
This paper provides an insight into the possibility of how to find ontologies
most relevant to scientific texts using artificial neural networks. The basic
idea of the presented approach is to select a representative paragraph from a
source text file, embed it to a vector space by a pre-trained fine-tuned
transformer, and classify the embedded vector according to its relevance to a
target ontology. We have considered different classifiers to categorize the
output from the transformer, in particular random forest, support vector
machine, multilayer perceptron, k-nearest neighbors, and Gaussian process
classifiers. Their suitability has been evaluated in a use case with ontologies
and scientific texts concerning catalysis research. From results we can say the
worst results have random forest. The best results in this task brought support
vector machine classifier