In this poster we describe a pilot study of searching social science literature for legacy corpora to evaluate text mining algorithms. The new emerging field of computational social science demands large amount of social science data to train and evaluate computational models. We argue that the legacy corpora that were annotated by social science researchers through traditional Qualitative Data...

Error estimation is a crucial part of classification methodology and it becomes problematic with small samples. We demonstrate here that the complexity of the decision boundary plays a key role on the performance of error estimation methods. First, a model is developed which quantifies the complexity of a classification problem purely in terms of the geometry of the decision boundary, without r...

Intermediate-band solar cells using quantum dot arrays (QDAs) are theoretically predicted to significantly increase the efficiency with which solar energy can be harvested. In the limit of identical quantum dots, the wavefunction for electrons in a QDA will be fully delocalized. Fully delocalized wavefunctions have been theoretically shown to reduce thermal losses and consequently increase phot...

The difficulty of multi-class classification generally increases with the number of classes. Using data from a subset of the classes, can we predict how well a classifier will scale with an increased number of classes? Under the assumption that the classes are sampled exchangeably, and under the assumption that the classifier is generative (e.g. QDA or Naive Bayes), we show that the expected ac...

The aim of the present study was to investigate the volatile and sensorial profile of basil (Ocimum basilicum L.) by quantitative descriptive analysis (QDA) of the essential oil of three hybrids ("Cinnamon" × "Maria Bonita," "Sweet Dani" × "Cinnamon," and "Sweet Dani" × "Maria Bonita"). Twelve descriptive terms were developed by a selected panel that also generated the definition of each term a...

This paper proposes a new representation space, called the cluster space, for data points that originate from high dimensions. Whereas existing dedicated methods concentrate on revealing manifolds from within the data, we consider here the context of clustered data and derive the dimension reduction process from cluster information. Points are represented in the cluster space by means of their ...

Results a) Problems: young MSMs & TGs engage in unprotected anal intercourse (80.9%), risk sex with people unknown of their HIV status (66%), drug and tobacco (59.6%) and alcohol consumption (55.3%). All participants perceived that MSM & TGs don’t use condoms consistently. They also experience mental health problems like anxiety and depression (80.9%). b) Associated factors: QDA outlined follow...

Feature selection is frequently used as a pre-processing step to machine learning. It is a process of choosing a subset of original features so that the feature space is optimally reduced according to a certain evaluation criterion. The central objective of this paper is to reduce the dimension of the data by finding a small set of important features which can give good classification performan...

This paper presents a comparative case study on the classification accuracy between 5 methods for golf putting performance analysis. In a previous work, a digital camera was used to capture 30 trials of 6 expert golf players. The detection of the horizontal position of the golf club was performed using a computer vision technique followed by the estimation algorithm Darwinian Particle Swarm Opt...