This article describes the construction of the local logistic classifier, a highly scalable and parallelizable classifier combining local learning with a logistic classifier. To speed up the learning of each local logistic classifier, a novel fixed point algorithm is introduced. The practicality and performance of the classifier is demonstrated on a 10 million character training set derived fro...

A novel two-stage construction algorithm for linear-in-the-parameters classifier is proposed, aiming at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage to construct a sparse linear-in-the-parameters classifier. For the first stage learning of generating the prefiltered signal, a tw...

introduction: raman spectroscopy, that is a spectroscopic technique based on inelastic scattering of monochromatic light, can provide valuable information about molecular vibrations, so using this technique we can study molecular changes in a sample. material and methods: in this research, 153 raman spectra obtained from normal and dried skin samples. baseline and electrical noise were eliminat...

In this paper, we develop a target detection algorithm based on a supervised learning technique that maximizes the margin between two classes, i.e., the target class and the non-target class. Specifically, our target detection algorithm consists of 1) image differencing, 2) maximum-margin classifier, and 3) diversity combining. The image differencing is to enhance and highlight the targets so t...

We present a new classification algorithm capable of learning from data corrupted by a class dependent uniform classification noise. The produced classifier is a linear classifier, and the algorithm works seamlessly when using kernels. The algorithm relies on the sampling of random hyperplanes that help the building of new training examples of which the correct classes are known; a linear class...

This paper presents an R package, arulesCBA, which uses association rules mined with the apriori algorithm from arules to build a classifier for discrete or transactional data sets. The package also provides an interface to use an association-rule classifier to predict classes for new data entries. The classification algorithm implemented in arulesCBA performs competitively when compared to exi...

Aim: The forecast of Myocardial Infarction for humans employing a Machine learning model by corresponding Logistic Regression Algorithm with CatBoost Classifier. accuracy is enhanced utilizing the novel LR Materials and Methods: study utilized total 20 sample iterations, 10 samples per group. Group 1 was analyzed using logistic regression algorithm, while 2 decision tree classifier. statistical...