This project compares the supervised logistic regression segmentation algorithm against the unsupervised k-means clustering segmentation. We observed that the difference between either method is not very significant. When performed on the 100 test cases for BSD300, the supervised method on average achieved a precision rate of 0.47 and the unsupervised method achieved a precision rate of 0.41. T...

Feature selection is a task of fundamental importance for many data mining or machine learning applications, including regression. Surprisingly, most of the existing feature selection algorithms assume the problems to address are either supervised or unsupervised, while supervised and unsupervised samples are often simultaneously available in real-world applications. Semi-supervised feature sel...

We study unsupervised and supervised recognition of human actions in video sequences. The videos are represented by probability distributions and then meaningfully compared in a probabilistic framework. We introduce two novel approaches outperforming state-of-the-art algorithms when tested on the KTH and Weizmann public datasets: an unsupervised nonparametric kernel-based method exploiting the ...

This paper studies the application of text similarity methods to disambiguate ambiguous links between WordNet nouns and Wikipedia categories. The methods range from word overlap between glosses, random projections, WordNetbased similarity, and a full-fledged textual entailment system. Both unsupervised and supervised combinations have been tried. The goldstandard with disambiguated links is pub...

Reducing the reliance of semantic role labeling (SRL) methods on human-annotated data has become an active area of research. However, the prior work has largely focused on either (1) looking into ways to improve supervised SRL systems by producing surrogate annotated data and reducing sparsity of lexical features or (2) considering completely unsupervised semantic role induction settings. In th...

Neural networks are split into 2 main categories; namely supervised & unsupervised In supervised learning the neural network is pravided with the desired response for a particular example. The unsupervised neural network does not require the desired response but determines itself what properties exist and learns to reflect these properties in its output. This has successfully been used for segm...

BACKGROUND A 42-year-old woman with chronic polymyositis complicated by post-myocarditis cardiomyopathy underwent supervised and unsupervised exercise therapy with staged increases in intensity. METHODS Supervised exercise therapy, which included adopted standards for patients with heart failure, was performed for 6 months. After one month, unsupervised exercise therapy was commenced, in the ...

In this paper, we present a comparison of unsupervised and supervised methods for key-phrase extraction from a domain corpus. The experimented unsupervised methods employ individual statistical measures and graph-based measures while the supervised methods apply machine learning models that include combinations of these statistical and graph-based measures. Graph-based measures are applied on a...