Automated report generation from agricultural robot log data

Note: some parts of the abstract have been censored to hide company secrets.<br/><br/>This project was performed for Orchid N.V., an innovator in agriculture. The project focuses on their Scalar system. This is a system that consists out of multiple machines that automatically feed cows. While farmers are generally pleased with these systems, --...

Edit Distance-Based Classification of Symbol Sequences

There are many types of sequences on which classification algorithms are applied. Sequences of symbols with information on the relation between every two direct successors (referred to as link information) is one of these. A common approach for classification of such sequences is to only consider the symbols and disregard the link information....

Predicting outdated payment cards: A white box approach

Over the last few years machine learning has become popular among businesses. The insights and predictions given by the classifiers can help people make better decisions. The field of machine learning research is also highly active. Researchers frequently come up with clever tweaks or improvements that increase the accuracy of certain...

Gaussian Process Regression for Long-Term Time Series Forecasting

Long-term time series forecasting has found many utilities in various domains. Nevertheless, it remains difficult to perform by many existing methods. One of the most well-known forecasting techniques, the ARIMA, does not suffice the long-term forecasting task due to the mean convergence problem. Therefore, this research empirically assesses the...

Regularizing AdaBoost to prevent overfitting on label noise

In this work, we have shown that AdaBoost is prone to overfitting when the training set contains mislabeled objects. We proposed that this is in part because the error estimate used to weight base classifiers and (indirectly) objects is biased in this scenario. We have shown that an unbiased estimator can prevent the overfitting, but such an...

Improving three aspects of a content based image retrieval system

A content based image retrieval system directly accepts image(s) as input, and returns similar images to the user. The key step in the searching process for a content based image retrieval system is calculating the similarities between the query image and the images in the database, follow by generating a ranked list based on the similarities....

Computer-based recognition of intertextuality within the Hebrew Bible

This research examines three approaches to recognize intertextuality. Two of them are novel because of their involving of the special features. The first approach uses multiple distance metrics. These distance metrics measures the similarity between strings. The second approach uses high-level grammatical features. These features are based on...

Dependency-based anomaly detection

Anomaly (or outlier) detection techniques can be used to find occurrences in data that are surprising or unusual, arousing the suspicion of being generated by an aberrant mechanism. A collective anomaly is a collection of data instances of which the individual data instances may not be anomalies by themselves, but their occurrence together is...

Using the Multiple Instance Learning framework to address differential regulation

Cell differentiation is a natural process occurring in all higher organisms, since the early fetal stage of life. It is, also, a part of disease – such as cancer – as the cell cycle becomes deregulated and cells behave differently compared to healthy ones. Differentiation occurs although the genome of all cells is identical across all cell types...

Interpretable classification of tumours through multiple instance learning and somatic mutations

Next generation sequencing is brought into the clinic. Screening of disease associated genes will aid the diagnosis of disorders with a genetic component. The diagnosis of cancer is of particular interest due to its variety and prevalence. The obtained mutations provide clues about underlying biological properties that could be used for...

Robust Automatic Object Detection in a Maritime Environment: Polynomial background estimation and the reduction of false detections by means of classification

Robust automatic detection of surface and air objects in a maritime environment is a problem that is of growing importance to the Royal Netherlands Navy (RNLN). Due to a shift in the field of operation from the open oceans towards the littoral waters, the RNLN is forced to operate in complex environments with cluttered backgrounds and the...

Inferring Arithmetic Expressions from Data

We present a framework for learning arithmetic expressions from a set of observations. Our intention is to introduce a Bayesian method for what is known as equation discovery. Our method is based on measuring a degree of belief (posterior probability) for a set of hypothesized expressions to find those which best explain the observed data. This...

Streampaper

Dit stageverslag is ter afsluiting van het bachelorproject voor de opleiding Technische Informatica aan de Technische Universiteit Delft. Gedurende de stage is het programma Streampaper ontworpen en geïmplementeerd. Streampaper is een op zichzelf staande applicatie waarin verschillende externe informatiestromen gecombineerd kunnen worden...

Qualitative Evaluation of Tracking Systems: A Model based approach

Object Tracking has been a very active area in the field of C omputer Vision. Over the years, a variety of approaches have been put forth to solve this problem and though many of them have demonstrate considerable success none of them have been completely successful. With more methods being written each day, the evaluation of such systems...

Using Dynamic Bayesian Networks for Posed versus Spontaneous Facial Expression Recognition

Automatic analysis of facial expressions is a complex area of pattern recognition and computer vision with many un- resolved problems, one of which is the distinction between posed and spontaneous expressions of emotions. Previous psychology research indicates that the temporal dynamics in the face are essential for distinguishing between posed...

Enhanced Question Classification with Optimal Combination of Features

An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. Question classification is typically done using machine learning techniques. Different lexical, syntactical and semantic features can be extracted from a...

Random Subspace Method for One-Class Classifiers