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2004

Greiner, Russ, Guo, Yuhong

Technical report TR04-22. Model selection problem in Bayesian belief network (BN) structure learning is a classicial problem in the BN literature. To do model selection in BN structure learning, we need a evaluation score and a searching procedure. The generative criteria, AIC, BIC and BDe, are...

2003

Greiner, Russ, Poulin, B., Lu, Paul, Anvik, J., Lu, Z., Macdonell, Cam, Wishart, David, Eisner, Roman, Szafron, Duane

Technical report TR03-09. Naive Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label. We present a...

2003

Greiner, Russell, Wishart, David, Eisner, Roman, Lu, Z., Lu, Paul, Macdonell, Cam, Poulin, B., Szafron, Duane, Anvik, J.

Technical report TR03-14. Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy...

2003

Lu, Z., Eisner, Roman, Lu, Paul, Macdonell, Cam, Szafron, Duane, Greiner, Russell, Poulin, B., Wishart, David, Anvik, J., Habibi-Nazhad, B.

Technical report TR03-05. Modern sequencing technology now permits the sequencing of entire genomes, leading to thousands of new gene sequences in need of detailed annotation. It is too time consuming to predict the properties of each protein sequence manually and to organize the results of many...

2007

Van Allen, Tim, Greiner, Russell, Singh, Ajit, Hooper, Peter

Technical report TR07-11. A Bayesian belief network models a joint distribution over variables using a DAG to represent variable dependencies and network parameters to represent the conditional probability of each variable given an assignment to its immediate parents. Existing algorithms assume...

2005

Greiner, Russell, Schmidt, Mark, Levner, Ilya

Technical report TR05-19. Detecting and segmenting brain tumors in Magnetic Resonance Images (MRI) is an important but time-consuming task performed by medical experts. Automating this process is a challenging task due to the often high degree of intensity and textural similarity between normal...