6,259 research outputs found
Monetary Politics in a Monetary Union: A Note on Common Agency with Rational Expectations
Is the politicisation of monetary policy in a currency union desirable? This paper shows that in a setting where political influence by national governments is modeled as a common agency game with rational expectations, the answer to this question crucially depends on whether the common central bank can commit to follow its policy.Common Agency, Political Pressures, European Monetary Union
Neural Network Ensembles for Time Series Prediction
Rapidly evolving businesses generate massive
amounts of time-stamped data sequences and defy a demand
for massively multivariate time series analysis. For such data
the predictive engine shifts from the historical auto-regression
to modelling complex non-linear relationships between multidimensional
features and the time series outputs. In order to
exploit these time-disparate relationships for the improved time
series forecasting, the system requires a flexible methodology
of combining multiple prediction models applied to multiple
versions of the temporal data under significant noise component
and variable temporal depth of predictions. In reply
to this challenge a composite time series prediction model
is proposed which combines the strength of multiple neural
network (NN) regressors applied to the temporally varied
feature subsets and the postprocessing smoothing of outputs
developed to further reduce noise. The key strength of the model
is its excellent adaptability and generalisation ability achieved
through a highly diversified set of complementary NN models.
The model has been evaluated within NISIS Competition 2006
and NN3 Competition 2007 concerning prediction of univariate
and multivariate time-series. It showed the best predictive
performance among 12 competitive models in the NISIS 2006
and is under evaluation within NN3 2007 Competition
Nature-Inspired Learning Models
Intelligent learning mechanisms found in natural world are still unsurpassed in their learning performance and eficiency of dealing with uncertain information coming in a variety of forms, yet remain under continuous challenge
from human driven artificial intelligence methods. This work intends to demonstrate how the phenomena observed in physical world can be directly used to guide artificial learning models. An inspiration for the new
learning methods has been found in the mechanics of physical fields found in both micro and macro scale.
Exploiting the analogies between data and particles subjected to gravity, electrostatic and gas particle fields, new algorithms have been developed and applied to classification and clustering while the properties of the
field further reused in regression and visualisation of classification and classifier fusion. The paper covers extensive pictorial examples and visual interpretations of the presented techniques along with some testing over
the well-known real and artificial datasets, compared when possible to the traditional methods
An Overview of Classifier Fusion Methods
A number of classifier fusion methods have been
recently developed opening an alternative approach
leading to a potential improvement in the
classification performance. As there is little theory of
information fusion itself, currently we are faced with
different methods designed for different problems and
producing different results. This paper gives an
overview of classifier fusion methods and attempts to
identify new trends that may dominate this area of
research in future. A taxonomy of fusion methods
trying to bring some order into the existing âpudding
of diversitiesâ is also provided
An Overview of Classifier Fusion Methods
A number of classifier fusion methods have been
recently developed opening an alternative approach
leading to a potential improvement in the
classification performance. As there is little theory of
information fusion itself, currently we are faced with
different methods designed for different problems and
producing different results. This paper gives an
overview of classifier fusion methods and attempts to
identify new trends that may dominate this area of
research in future. A taxonomy of fusion methods
trying to bring some order into the existing âpudding
of diversitiesâ is also provided
Which entrepreneurs expect to expand their businesses? Evidence from survey data in Lithuania
This paper presents an empirical study based on a survey of 399 small and medium size companies in Lithuania. Applying bivariate and ordered probit estimators, we investigate why some business owners intend to expand their firms, while others do not. Our main findings provide evidence that the characteristics of the owners matter. Those with higher education and âlearning by doingâ attributes either through previous job experience or additional entrepreneurial experience are more likely to expand their businesses. In addition, the model implications include that the intentions to expand are correlated with exporting and with size of the enterprise: medium and small size companies are more likely to grow than micro enterprises and self-employed entrepreneurs. We also analyse the link between the main perceptions of constraints to business activities and growth expectations and find that the factors, which are perceived as main business barriers, are not necessary those, which are associated with low growth expectations. In particular, perceptions of both corruption and of inadequate tax systems are main barriers to growth.http://deepblue.lib.umich.edu/bitstream/2027.42/40109/3/wp723.pd
Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems
Combining classifiers by majority voting (MV) has
recently emerged as an effective way of improving
performance of individual classifiers. However, the
usefulness of applying MV is not always observed and
is subject to distribution of classification outputs in a
multiple classifier system (MCS). Evaluation of MV
errors (MVE) for all combinations of classifiers in MCS
is a complex process of exponential complexity.
Reduction of this complexity can be achieved provided
the explicit relationship between MVE and any other
less complex function operating on classifier outputs is
found. Diversity measures operating on binary
classification outputs (correct/incorrect) are studied in
this paper as potential candidates for such functions.
Their correlation with MVE, interpreted as the quality
of a measure, is thoroughly investigated using artificial
and real-world datasets. Moreover, we propose new
diversity measure efficiently exploiting information
coming from the whole MCS, rather than its part, for
which it is applied
The brachyopoid Hadrokkosaurus bradyi from the early Middle Triassic of Arizona, and a phylogenetic analysis of lower jaw characters in temnospondyl amphibians
The holotype of the brachyopoid temnospondyl Hadrokkosaurus bradyi, represented by a right lower jaw ramus, is reâexâ
amined based upon new data and revision of various morphological features. Additional fragmentary jaw material reâ
ferred to this species is briefly described. Prominent features are a large postsymphyseal foramen that is anteriorly open,
and prearticular and surangular buttresses for support of the articular. Brachyopoid characters include a long and robust
postglenoid area formed by surangular and prearticular, anterior and posterior keels on at least some marginal dentary
teeth, and subtriangular outline of the adductor fossa in dorsal view. Five features of the holotype ramus, long thought to
be at odds with its brachyopoid or temnospondyl nature, are critically reâevaluated. A phylogenetic analysis of lower jaw
characters in temnospondyls retrieves most of the clades found in more comprehensive data sets, but the statistical node
support is low. Brachyopoids are monophyletic, with Hadrokkosaurus emerging as their most basal taxon
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