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We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naïve Bayes classifier on… (More)

- Andrew I. Schein, Lawrence K. Saul, Lyle H. Ungar
- AISTATS
- 2003

We investigate a generalized linear model for dimensionality reduction of binary data. The model is related to principal component analysis (PCA) in the same way that logistic regression is related… (More)

- Seth Kulick, Ann Bies, +7 authors Peter White
- 2004

We describe an approachto two areas of biomedicalinformationextraction,drugdevelopmentandcancergenomics.We have developedaframework whichincludescorpusannotationintegratedat multiple levels: a… (More)

- Andrew I. Schein, Jessica C. Kissinger, Lyle H. Ungar
- Nucleic acids research
- 2001

Previous work in predicting protein localization to the chloroplast organelle in plants led to the development of an artificial neural network-based approach capable of remarkable accuracy in its… (More)

- Andrew I. Schein, Lyle H. Ungar
- Machine Learning
- 2007

Which active learning methods can we expect to yield good performance in learning binary and multi-category logistic regression classifiers? Addressing this question is a natural first step in… (More)

- Jinying Chen, Andrew I. Schein, Lyle H. Ungar, Martha Palmer
- HLT-NAACL
- 2006

This paper shows that two uncertaintybased active learning methods, combined with a maximum entropy model, work well on learning English verb senses. Data analysis on the learning process, based on… (More)

ACTIVE LEARNING FOR LOGISTIC REGRESSION Andrew Ian Schein Supervisor: Lyle H. Ungar Which active learning methods can we expect to yield good performance in learning logistic regression classifiers?… (More)

Systems for automati ally re ommending items (e.g., movies, produ ts, or information) to users are be oming in reasingly important in eommer e appli ations, digital libraries, and other domains where… (More)

The two-way aspect model is a latent class statistical mixture model for performing soft clustering of cooccurrence data observations. It acts on data such as document/word pairs (words occurring in… (More)

- Grant Gehrke, Craig H. Martell, Andrew I. Schein, Pranav Anand
- Int. J. Semantic Computing
- 2009