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ICML
2009
IEEE
10 years 10 months ago
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-sized, high-dimensional images re...
Honglak Lee, Roger Grosse, Rajesh Ranganath, Andre...
ICML
2009
IEEE
10 years 10 months ago
Large margin training for hidden Markov models with partially observed states
Large margin learning of Continuous Density HMMs with a partially labeled dataset has been extensively studied in the speech and handwriting recognition fields. Yet due to the non...
Thierry Artières, Trinh Minh Tri Do
ICML
2009
IEEE
10 years 10 months ago
A majorization-minimization algorithm for (multiple) hyperparameter learning
We present a general Bayesian framework for hyperparameter tuning in L2-regularized supervised learning models. Paradoxically, our algorithm works by first analytically integratin...
Chuan-Sheng Foo, Chuong B. Do, Andrew Y. Ng
ICML
2009
IEEE
10 years 10 months ago
Polyhedral outer approximations with application to natural language parsing
Recent approaches to learning structured predictors often require approximate inference for tractability; yet its effects on the learned model are unclear. Meanwhile, most learnin...
André F. T. Martins, Noah A. Smith, Eric P....
ICML
2009
IEEE
10 years 10 months ago
Identifying suspicious URLs: an application of large-scale online learning
This paper explores online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. W...
Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffr...
ICML
2009
IEEE
10 years 10 months ago
Approximate inference for planning in stochastic relational worlds
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
Tobias Lang, Marc Toussaint
ICML
2009
IEEE
10 years 10 months ago
Generalization analysis of listwise learning-to-rank algorithms
This paper presents a theoretical framework for ranking, and demonstrates how to perform generalization analysis of listwise ranking algorithms using the framework. Many learning-...
Yanyan Lan, Tie-Yan Liu, Zhiming Ma, Hang Li
ICML
2009
IEEE
10 years 10 months ago
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
Information theoretic based measures form a fundamental class of similarity measures for comparing clusterings, beside the class of pair-counting based and set-matching based meas...
Xuan Vinh Nguyen, Julien Epps, James Bailey
ICML
2009
IEEE
10 years 10 months ago
Boosting with structural sparsity
Despite popular belief, boosting algorithms and related coordinate descent methods are prone to overfitting. We derive modifications to AdaBoost and related gradient-based coordin...
John Duchi, Yoram Singer
ICML
2009
IEEE
10 years 10 months ago
Structure learning of Bayesian networks using constraints
This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...
Cassio Polpo de Campos, Zhi Zeng, Qiang Ji
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