This paper uses a data mining approach to the prediction of corporate failure. Initially, we use four single classi®ers Ð discriminant analysis, logistic regression, neural networks and C5.0 Ð each based on two feature selection methods for predicting corporate failure. Of the two feature selection methods Ð human judgement based on ®nancial theory and ANOVA statistical method Ð we found the AN...

We define and study analogues of exponentials for functions on noncommutative two-tori that depend on a choice of a complex structure. The major difference with the commutative case is that our noncommutative exponentials can be defined only for sufficiently small functions. We show that this phenomenon is related to the existence of certain discriminant hypersurfaces in an irrational rotation ...

Motivated by the analogies to statistical physics, the deterministic annealing (DA) method has successfully been demonstrated in a variety of application. In this paper, we explore a new methodology to devise the classifier under the DA method. The differential cost function is derived subject to a constraint on the randomness of the solution, which is governed by the temperature T . While grad...

The significant development of credit industry led to growing interest in sophisticated methods which can support making more accurate and more rapid credit decisions. The parametric statistical methods such as linear discriminant analysis and logistic regression were soon followed up by nonparametrical methods and other techniques: neural networks, decision trees, and genetic algorithms. This ...

FIGHT VARIABLES by JEREMIAH DOUGLAS JOHNSON (Under the Direction of Daniel B. Hall) ABSTRACT In this study, I attempt to forecast the win/loss outcomes of mixed martial arts bouts with fight data. Both basic ‘count’ variables and newer, constructed variables are considered. These novel measures are then used to predict wins and losses using a logistic regression model, and this model is compare...

The objective of the proposed study is to explore the performance of credit scoring using a two-stage hybrid modeling procedure with artificial neural networks and multivariate adaptive regression splines (MARS). The rationale under the analyses is firstly to use MARS in building the credit scoring model, the obtained significant variables are then served as the input nodes of the neural networ...

This article describes the systems jointly submitted by Institute for Infocomm (IR), the Laboratoire d’Informatique de l’Universit du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE). The submitted system is a fusion of nine sub-systems based on i-vectors [1] extracted from different types of featur...

This paper focuses on discriminative trainings (DT) applied to ivectors after Gaussian probabilistic linear discriminant analysis (PLDA). If DT has been successfully used with non-normalized vectors, this technique struggles to improve speaker detection when i-vectors have been first normalized, whereas the latter option has proven to achieve best performance in speaker verification. We propose...

Credit scoring systems are currently in common use by numerous financial institutions worldwide. However, credit scoring with the microfinance industry is a relatively recent application, and no model which employs a non-parametric statistical technique has yet, to the best of our knowledge, been published. This lack is surprising since the implementation of credit scoring should contribute tow...

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two mach...