Modified Large Margin Nearest Neighbor Metric Learning for Regression

The main objective of this letter is to formulate a new approach of learning a Mahalanobis distance metric for nearest neighbor regression from a training sample set. We propose a modified version of the large margin nearest neighbor metric learning method to deal with regression problems. As an application, the prediction of post-operative trunk 3-D shapes in scoliosis surgery using nearest neighbor regression is described. Accuracy of the proposed method is quantitatively evaluated through experiments on real medical data.IRSC / CIH

Statistical model based 3D shape prediction of postoperative trunks for non-invasive scoliosis surgery planning

One of the major concerns of scoliosis patients undergoing surgical treatment is the aesthetic aspect of the surgery outcome. It would be useful to predict the postoperative appearance of the patient trunk in the course of a surgery planning process in order to take into account the expectations of the patient. In this paper, we propose to use least squares support vector regression for the prediction of the postoperative trunk 3D shape after spine surgery for adolescent idiopathic scoliosis. Five dimensionality reduction techniques used in conjunction with the support vector machine are compared. The methods are evaluated in terms of their accuracy, based on the leave-one-out cross-validation performed on a database of 141 cases. The results indicate that the 3D shape predictions using a dimensionality reduction obtained by simultaneous decomposition of the predictors and response variables have the best accuracy.CIHR / IRS

The Effect of Competition on the Financial Sustainability of Micro Finance Institutions in Cameroon

This study aimed at investigating the effect of competition on the sustainability of Microfinance Institutions (MFIs) in Cameroon. Secondary data collected from the market mix data set was used for the study. The Herfindhal-Hirschman Index (HHI) was used to estimate the concentration (competition) index for MFIs while the random effect model was used as the estimation technique based on the Hausmann test. The results showed that an increase in concentration had a negative and statistical significant effect on return on asset. The result implies that as competition in MFIs increases, financial sustainability also improves. Other results showed that staff productivity, outreach, capital adequacy had a positive effect on financial sustainability while portfolio at risk had a negative effect on financial sustainability. The study therefore advocates for policies that can promote an environment conducive for competition in order to encourage MFIs to adopt innovative strategies to remain sustainable. Keywords: Competition, Sustainability, MFIs, Cameroon JEL Classification: G21, L1, O1

Complexity of XOR/XNOR boolean functions: a model using binary decision diagrams and back propagation neural networks

This paper proposes a model that predicts the complexity of Boolean functions with only XOR/XNOR min-terms using back propagation neural networks (BPNNs) applied to Binary Decision Diagrams (BDDs). The BPNN model (BPNNM) is developed through the training process of experimental data already obtained for XOR/XNOR-based Boolean functions. The outcome of this model is a unique matrix for the complexity estimation over a set of BDDs derived from Boolean expressions with a given number of variables and XOR/XNOR min-terms. The comparison results of the experimental and BPNNM underline the efficiency of this approach, which is capable of providing some useful clues about the complexity of the circuit to be implemented. It also proves the computational capabilities of NNs in providing reliable classification of the complexity of Boolean functions.Facultad de Informátic