Machine Learning Methods for Neonatal Mortality and Morbidity Classification

Machine Learning Methods for Climbing Route Classification

Classifying the difficulty of climbing routes is a challenging and subjective task, even for experienced climbers. This project builds on previous work [6] and evaluated several algorithms to aid in this process. Data was sourced from a standard climbing wall (the Moonboard) found in gyms around the world. Three different models were explored for classification: Naive Bayes, softmax regression,...

Machine Learning methods for E-mail Classification

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable antispam filters. Using a classifier based on machine learning techniques to automatically filter out spam email has drawn many researchers attention. In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system...

Machine Learning Methods for Fault Classification

With the constant evolution and ever-increasing transistor densities in semiconductor technology, error rates are on the rise. Errors that occur on semiconductor chips can be attributed to permanent, transient or intermittent faults. Out of these errors, once permanent errors appear, they do not go away and once intermittent faults appear on chips, the probability that they will occur again is ...

Sequence function classification by machine learning methods

An evaluation of machine-learning methods for predicting pneumonia mortality

This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted...