Personal names are important and common information in many data sources, ranging from social networks and news articles to patient records and scientific documents. They are often used as queries for retrieving records and also as key information for linking documents from multiple sources. Matching personal names can be challenging due to variations in spelling and various formatting of names...

This paper presents the design of a system for personalization of contents and advertising for readers of online newspapers. This software is conceived to work in a context of high network traffic with millions of URLs served each day. The model is divided into two subsystems. The first one takes care of the recommendation of news items. The mathematical model is based on the PageRank algorithm...

Due to the low information content of individual SAR images, single-band SAR data do not provide highly accurate land cover classification. However, in areas under risk where rapid land cover mapping is required, the advantages of SAR which include cloud penetration and day/night acquisition, are evident in comparison to optical data. The main research goal of this study is to fuse different fr...

I propose to study a network of coupled logistic maps that follow a simple rule to change links similar to the models in [1, 2]. What distinguishes the model I will study is the rule for rewiring links, which is more realistic and less stochastic than that in [1, 2]. I will study the network that results from these interacting logistic maps—and compare the results to those in [1, 2]—and, time p...

Using a derivation data set of 1253 patients, we built several logistic regression and neural network models to estimate the likelihood of myocardial infarction based upon patient-reportable clinical history factors only. The best performing logistic regression model and neural network model had C-indices of 0.8444 and 0.8503, respectively, when validated on an independent data set of 500 patie...

This paper presents a tutorial introduction to the logistic function as a statistical object. Beyond the discussion of the whys and wherefores of the logistic function, I also hope to illuminate the general distinction between the \generative/causal/class-conditional" and the \discriminative/diagnostic/ predictive" directions for the modeling of data. Crudely put, the belief network community h...

Logistic regression LR is a conventional statistical technique used for data classification problem. Logistic regression is a model-based method, and it uses nonlinear model structure. Another technique used for classification is feedforward artificial neural networks. Feedforward artificial neural network is a data-based method which can model nonlinear models through its activation function. ...

Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non...

logistic regression models are frequently used in clinicalresearch and particularly for modeling disease status and patientsurvival. in practice, clinical studies have several limitationsfor instance, in the study of rare diseases or due ethical considerations, we can only have small sample sizes. in addition, the lack of suitable andadvanced measuring instruments lead to non-precise observatio...