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» Learning to Solve QBF
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IJCNN
2006
IEEE
15 years 10 months ago
Model Selection via Bilevel Optimization
— A key step in many statistical learning methods used in machine learning involves solving a convex optimization problem containing one or more hyper-parameters that must be sel...
Kristin P. Bennett, Jing Hu, Xiaoyun Ji, Gautam Ku...
144
Voted
AISC
2010
Springer
15 years 8 months ago
Adapting Mathematical Domain Reasoners
Abstract. Mathematical learning environments help students in mastering mathematical knowledge. Mature environments typically offer thousands of interactive exercises. Providing f...
Bastiaan Heeren, Johan Jeuring
AAAI
2006
15 years 5 months ago
Efficient L1 Regularized Logistic Regression
L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized...
Su-In Lee, Honglak Lee, Pieter Abbeel, Andrew Y. N...
138
Voted
CVPR
2008
IEEE
16 years 5 months ago
Spectral methods for semi-supervised manifold learning
Given a finite number of data points sampled from a low-dimensional manifold embedded in a high dimensional space together with the parameter vectors for a subset of the data poin...
Zhenyue Zhang, Hongyuan Zha, Min Zhang
ICML
2009
IEEE
16 years 4 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