Exam in Statistical Machine Learning Statistisk Maskininlärning

Statistical Machine Learning – Solutions for Exam 2017-06-09

Machine learning algorithms in air quality modeling

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...

Second Exam: Natural Language Parsing with Neural Networks

With the advent of “deep learning”, there has been a recent resurgence of interest in the use of artificial neural networks for machine learning. This paper presents an overview of recent research in the statistical parsing of natural language sentences using such neural networks as a learning model. Though it is a fairly new addition to the toolset in this area, important results have been rec...

Comparing Statistical and Machine Learning Classifiers: Alternatives for Predictive Modeling in Human Factors Research

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...

Application of ensemble learning techniques to model the atmospheric concentration of SO2

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...