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This paper describes two different approaches for incorporating background knowledge into nearest-neighbor text classification. Our first approach uses background text to assess the similarity between training and test documents rather than assessing their similarity directly. The second method redescribes examples using Latent Semantic Indexing on the background knowledge, assessing document similarities...
The work described in this paper applies machine learning techniques, to a database of accommodative esotropic patients. Accommodative esotropia is an eye disease that when left untreated leads to blindness. Patients whose muscles deteriorate most often need corrective surgery in order to prevent this, since less invasive methods of treatment tend to fail in these patients. It is often difficult for...
Tandem repeats in DNA sequences are extremely relevant in biological phenomena and diagnostic tools. Computational programs that discover these tandem repeats generate a huge volume of data, which is often difficult to decipher without further organization. In this paper, we describe a new method for post-processing tandem repeats through clustering. Our work presents multiple ways of expressing tandem...
In this paper, we describe a unique partnership between a computer science course and a history/U.S. government course at the College of Staten Island. A cohort of students take the science and history courses together, and lectures, laboratory assignments, and reports in each subject reinforce the concepts learned in the other one. The intent of this partnership is to encourage general education...
Classification of search queries is a complex and computationally challenging task. Typically, search queries are short, reveal very few features per single query and are therefore a weak source for traditional machine learning. In this paper, we present a method that combines limited manual labeling, computational linguistics and information retrieval to classify a large collection of Web search...
Intelligent use of the many diverse forms of data available on the Internet requires new tools for managing and manipulating heterogeneous forms of information. This paper uses WHIRL, an extension of relational databases that can manipulate textual data using statistical similarity measures developed by the information retrieval community. We show that although WHIRL is designed for more general similarity-based...
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