23,059 research outputs found
Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets
This paper develops a flexible multi-dimensional assessment method for the comparison of different statistical-econometric techniques based on learning mechanisms with a view to analysing and forecasting regional labour markets. The aim of this paper is twofold. A first major objective is to explore the use of a standard choice tool, namely Multicriteria Analysis (MCA), in order to cope with the intrinsic methodological uncertainty on the choice of a suitable statistical-econometric learning technique for regional labour market analysis. MCA is applied here to support choices on the performance of various models -based on classes of Neural Network (NN) techniques-that serve to generate employment forecasts in West Germany at a regional/district level. A second objective of the paper is to analyse the methodological potential of a blend of approaches (NN-MCA) in order to extend the analysis framework to other economic research domains, where formal models are not available, but where a variety of statistical data is present. The paper offers a basis for a more balanced judgement of the performance of rival statistical tests
Machine Learning tools for global PDF fits
The use of machine learning algorithms in theoretical and experimental
high-energy physics has experienced an impressive progress in recent years,
with applications from trigger selection to jet substructure classification and
detector simulation among many others. In this contribution, we review the
machine learning tools used in the NNPDF family of global QCD analyses. These
include multi-layer feed-forward neural networks for the model-independent
parametrisation of parton distributions and fragmentation functions, genetic
and covariance matrix adaptation algorithms for training and optimisation, and
closure testing for the systematic validation of the fitting methodology.Comment: 12 pages, 9 figures, to appear in the proceedings of the XXIIIth
Quark Confinement and the Hadron Spectrum conference, 1-6 August 2018,
University of Maynooth, Irelan
Classifiers With a Reject Option for Early Time-Series Classification
Early classification of time-series data in a dynamic environment is a
challenging problem of great importance in signal processing. This paper
proposes a classifier architecture with a reject option capable of online
decision making without the need to wait for the entire time series signal to
be present. The main idea is to classify an odor/gas signal with an acceptable
accuracy as early as possible. Instead of using posterior probability of a
classifier, the proposed method uses the "agreement" of an ensemble to decide
whether to accept or reject the candidate label. The introduced algorithm is
applied to the bio-chemistry problem of odor classification to build a novel
Electronic-Nose called Forefront-Nose. Experimental results on wind tunnel
test-bed facility confirms the robustness of the forefront-nose compared to the
standard classifiers from both earliness and recognition perspectives
- …