1,950 research outputs found
Ranking relations using analogies in biological and information networks
Analogical reasoning depends fundamentally on the ability to learn and
generalize about relations between objects. We develop an approach to
relational learning which, given a set of pairs of objects
,
measures how well other pairs A:B fit in with the set . Our work
addresses the following question: is the relation between objects A and B
analogous to those relations found in ? Such questions are
particularly relevant in information retrieval, where an investigator might
want to search for analogous pairs of objects that match the query set of
interest. There are many ways in which objects can be related, making the task
of measuring analogies very challenging. Our approach combines a similarity
measure on function spaces with Bayesian analysis to produce a ranking. It
requires data containing features of the objects of interest and a link matrix
specifying which relationships exist; no further attributes of such
relationships are necessary. We illustrate the potential of our method on text
analysis and information networks. An application on discovering functional
interactions between pairs of proteins is discussed in detail, where we show
that our approach can work in practice even if a small set of protein pairs is
provided.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS321 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Forecasting loss given default with the nearest neighbor algorithm
Mestrado em Matemática FinanceiraNos últimos anos, a previsão do Loss Given Default (LGD) tem sido um dos principais desafios no âmbito da gestão do risco de crédito. Investigadores académicos e profissionais da indústria bancária têm-se dedicado ao estudo deste parâmetro de risco em particular. Apesar de todas as diferentes abordagens já desenvolvidas e publicadas até hoje, a previsão do LGD continua a ser um tema de estudo académico intenso e sobre o qual ainda não existe um "consenso" metodológico na banca. Este trabalho apresenta uma abordagem alternativa para a previsão do LGD baseada na utilização de um simples, mas intuitivo, algoritmo de Machine Learning: o algoritmo nearest neighbor. De forma a avaliar a perfomance desta técnica não paramétrica na previsão do LGD, são utilizadas determinadas métricas de avaliação que permitem a comparação com um modelo paramétrico mais convencional e com a utilização do LGD médio histórico.In recent years, forecasting Loss Given Default (LGD) has been a major challenge in the field of credit risk management. Practitioners and academic researchers have focused on the study of this particular risk dimension. Despite all different approaches that have been developed and published so far, it remains an area of intense academic study and with lack of consensual solutions in the banking industry. This paper presents an LGD forecasting approach based on a simple and intuitive Machine Learning algorithm: the nearest neighbor algorithm. In order to evaluate the performance of this non parametric technique, some proper evaluation metrics are used to compare it to a more ?classical? parametric model and to the use of historical recovery rates to predict LGD
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