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Skip to Search Results- 2Online Learning
- 1Active learning
- 1Confidence Sets
- 1Costly Observations
- 1Fixed-depth decision tree
- 1Greedy Coordinate Descent
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Spring 2013
Multiple kernel learning (MKL) addresses the problem of learning the kernel function from data. Since a kernel function is associated with an underlying feature space, MKL can be considered as a systematic approach to feature selection. Many of the existing MKL algorithms perform kernel learning...
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Spring 2013
Many machine learning algorithms learn from the data by capturing certain interesting characteristics. Decision trees are used in many classification tasks. In some circumstances, we only want to consider fixed-depth trees. Unfortunately, finding the optimal depth-d decision tree can require time...
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Spring 2013
In a discrete-time online control problem, a learner makes an effort to control the state of an initially unknown environment so as to minimize the sum of the losses he suffers, where the losses are assumed to depend on the individual state-transitions. Various models of control problems have...
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Spring 2013
This work introduces the “online probing” problem: In each round, the learner is able to purchase the values of a subset of features for the current instance. After the learner uses this information to produce a prediction for this instance, it then has the option of paying for seeing the full...