Aynur A. Dayanik

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Supervised learning approaches to text classification are in practice often required to work with small and unsystematically collected training sets. The alternative to supervised learning is usually viewed to be building classifiers by hand, using a domain expert's understanding of which features of the text are related to the class of interest. This is(More)
Consider a supervised learning problem in which examples contain both numericaland textvalued features. To use traditional feature-vector-based learning methods, one could treat the presence or absence of a word as a Boolean feature and use these binary-valued features together with the numerical features. However, the use of a text-classification system on(More)
This paper presents EmailValet, a system that learns users’ emailreading preferences on email-capable wireless platforms – specifically, on two-way pagers with small ”qwerty” keyboards and an 8-line 30-character display. In use by the authors for about three months, it has gathered data on email-reading preferences over more than 8900 email messages(More)
Consider a supervised learning problem in which examples contain both numericaland text-valued features. To use traditional featurevector-based learning methods, one could treat the presence or absence of a word as a Boolean feature and use these binary-valued features together with the numerical features. However, the use of a text-classification system on(More)
This paper introduces Information Valets (“iValets”), a general framework for intelligent access to information. Our goal is to support access to a range of information sources from a range of client devices with some degree of uniformity. Further, the information server is aware of its user and user devices, learning from the user’s past interactions where(More)
Domain parsing, or the detection of signals of protein structural domains from sequence data, is a complex and difficult problem. If carried out reliably it would be a powerful interpretive and predictive tool for genomic and proteomic studies. We report on a novel approach to domain parsing using consensus techniques based on Hidden Markov Models (HMMs)(More)
This paper presents Feature Interval Learning algorithms (FIL) which represent multi-concept descriptions in the form of disjoint feature intervals. The FIL algorithms are batch supervised inductive learning algorithms and use feature projections of the training instances to represent induced classification knowledge. The concept description is learned(More)