Multiway clustering for creating biomedical term sets (2009)
Vasileios K, Lyle Ungar, Ted S, Shane Jensen
We present an EM-based clustering method that can be used for constructing or augmenting ontologies such as MeSH. Our algorithm simultaneously clusters verbs and nouns using both verb-noun and...
Research Track Paper Streaming Feature Selection using Alpha-investing ABSTRACT (2009)
Jing Zhou, Robert Stine, Dean Foster, Lyle Ungar
In Streaming Feature Selection (SFS), new features are sequentially considered for addition to a predictive model. When the space of potential features is large, SFS offers many advantages over...
Transfer Learning Using Feature Selection (2009)
Dhillon, Paramveer S, Foster, Dean, Ungar, Lyle
We present three related ways of using Transfer Learning to improve feature selection. The three methods address different problems, and hence share different kinds of information between tasks or...
Transfer Learning Using Feature Selection (2009)
Dhillon, Paramveer S., Foster, Dean, Ungar, Lyle
We present three related ways of using Transfer Learning to improve feature selection. The three methods address different problems, and hence share different kinds of information between tasks or...
Prediction of HIV-1 virus-host protein interactions using virus and host sequence motifs (2009)
Evans, Perry, Dampier, William, Ungar, Lyle, Tozeren, Aydin
Abstract Background Host protein-protein interaction networks are altered by invading virus proteins, which create new interactions, and modify or destroy others. The resulting network topology...
Prediction of HIV-1 virus-host protein interactions using virus and host sequence motifs (2009)
Evans, Perry, Dampier, William, Ungar, Lyle, Tozerin, Aydin
BackgroundHost protein-protein interaction networks are altered by invading virus proteins, which create new interactions, and modify or destroy others. The resulting network topology favors...
A predictive model for identifying mini-regulatory modules in the mouse genome (2009)
Yaragatti, Mahesh, Sandler, Ted, Ungar, Lyle
Motivation: Rapidly advancing genome technology has allowed access to a large number of diverse genomes and annotation data. We have defined a systems model that integrates assembly data, comparative...
MedlineDB: An integrated biological text mining framework (2008)
Abstract. MedlineDB is a schema and framework for integrating Medline abstracts with common biological organism databases for improved text analysis. The framework utilizes and extends existing,...
Rob Callan, Raymond Hsu, Steve Kimbrough, Lyle Ungar
This goal of this senior design project is to implement and analyze the effectiveness of different meta-heuristic algorithms on solving two constrained optimization problems: the Bin Packing Problem...
Patterns of sequence conservation in presynaptic neural genes (2006)
Hadley, Dexter, Murphy, Tara, Valladares, Otto, Hannenhalli, Sridhar, Ungar, Lyle, Kim, Junhyong, ...
Abstract Background The neuronal synapse is a fundamental functional unit in the central nervous system of animals. Because synaptic function is evolutionarily conserved, we reasoned that functional...
Open Access Research Patterns of sequence conservation in presynaptic neural genes (2006)
Dexter Hadley, Tara Murphy, Otto Valladares, Sridhar Hannenhalli, Lyle Ungar, Junhyong Kim, ...
electronic version of this article is the complete one and can be
An empirical study of the behavior of active learning for word sense disambiguation (2006)
Jinying Chen, Andrew Schein, Lyle Ungar, Martha Palmer
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...
Combining Linguistic and Statistical Analysis to Extract Relations from Web Documents (2006)
Suchanek, Fabian M., Ifrim, Georgiana, Weikum, Gerhard, Eliassi-Rad, Tina, Ungar, Lyle, Craven, Mark, ...
abstract 1: The World Wide Web provides a nearly endless source of knowledge, which is mostly given in natural language. A first step towards exploiting this data automatically could be to extract...
A-Optimality for Active Learning of Logistic Regression Classifiers (2004)
Over the last decade there has been growing interest in pool-based active learning techniques, where instead of receiving an i.i.d. sample from a pool of unlabeled data, a learner may take an active...
A Proposal for Learning by Ontological Leaps (2002)
Dean Foster And, Dean Foster, Lyle Ungar
this paper we combine feature creation, feature selection, and use of concept networks. We claim that many complex learning tasks require making what we term ontological leaps: the incorporation of...
Exploiting Multiple Secondary Reinforcers in Policy Gradient Reinforcement Learning (2001)
Most formulations of Reinforcement Learning depend on a single reinforcement reward value to guide the search for the optimal policy solution.
Localizing search in reinforcement learning (2000)
Reinforcement learning (RL) can be impractical for many high dimensional problems because of the computational cost of doing stochastic search in large state spaces. We propose a new RL method,...
Efficient Clustering of High-Dimensional Data Sets with Application to Reference Matching (2000)
Andrew Mccallum, Kamal Nigam, Lyle Ungar
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques for clustering when...
Efficient clustering of high-dimensional data sets with application to reference matching (2000)
Andrew McCallum, Kamal Nigam, Lyle Ungar
Many important problems involve clustering large data sets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques for clustering when...
Reinforcement learning (RL) can be impractical for many high dimensional problems because of the computational cost of doing stochastic search in large state spaces. We propose a new RL method,...
A Formal Statistical Approach to Collaborative Filtering (1998)
Grouping people into clusters based on the items they have purchased allows accurate recommendations of new items for purchase: If you and I have liked many of the same movies, then I will probably...
Prediction Intervals for Neural Networks Via Nonlinear Regression (1998)
Richard De Veaux, Jennifer Schumi, Jason Schweinsberg, David Shellington, Lyle Ungar
Standard methods for computing prediction intervals in nonlinear regression can be effectively applied to neural networks when the number of training points is large. However, simulations show that...
Estimating Prediction Intervals for Artificial Neural Networks (1996)
Lyle Ungar, Richard D. De, Veaux Evelyn Rosengarten
Neural networks can be viewed as nonlinear models, where the weights are parameters to be estimated. In general two parameter estimation methods are used: nonlinear regression, corresponding to the...
Patterns of sequence conservation in presynaptic neural genes
Hadley, Dexter, Murphy, Tara, Valladares, Otto, Hannenhalli, Sridhar, Ungar, Lyle, Kim, Junhyong, ...
Comparative sequence analysis and annotation of genomic regions surrounding 150 presynaptic genes identified over 26,000 elements highly conserved in eight vertebrate species; these results are made...