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Guo, Yuhong - Department of Computer and Information Sciences, Temple University
A Reformulation of Support Vector Machines for General Confidence Functions
Efficient Global Optimization for Exponential Family PCA and Low-Rank Matrix Factorization
Supervised Exponential Family Principal Component Analysis via Global Optimization
Max-Margin Multiple-Instance Learning via Semidefinite Programming
Convex Relaxations of Latent Variable Training Yuhong Guo and Dale Schuurmans
Learning Gene Regulatory Networks via Globally Regularized Risk Minimization
Optimistic Active Learning using Mutual Information Yuhong Guo and Russ Greiner
24 Metric-Based Approaches for Semi-Supervised Regression and Classification
Discriminative Model Selection for Belief Net Structures Yuhong Guo and Russ Greiner
Discriminative Batch Mode Active Learning Yuhong Guo and Dale Schuurmans
Learning Coordination Classifiers Yuhong Guo Russell Greiner Dale Schuurmans
Supervised Exponential Family Principal Component Analysis via Convex Optimization
Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering
Maximum Margin Bayesian Networks Department of Computing Science
