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» Learning to learn with the informative vector machine
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ICML
2010
IEEE
13 years 10 months ago
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is ...
Niranjan Srinivas, Andreas Krause, Sham Kakade, Ma...
ICML
2010
IEEE
13 years 10 months ago
A Theoretical Analysis of Feature Pooling in Visual Recognition
Many modern visual recognition algorithms incorporate a step of spatial `pooling', where the outputs of several nearby feature detectors are combined into a local or global `...
Y-Lan Boureau, Jean Ponce, Yann LeCun
ML
2006
ACM
13 years 9 months ago
XRules: An effective algorithm for structural classification of XML data
Abstract XML documents have recently become ubiquitous because of their varied applicability in a number of applications. Classification is an important problem in the data mining ...
Mohammed Javeed Zaki, Charu C. Aggarwal
TSE
2008
129views more  TSE 2008»
13 years 9 months ago
Classifying Software Changes: Clean or Buggy?
This paper introduces a new technique for predicting latent software bugs, called change classification. Change classification uses a machine learning classifier to determine wheth...
Sunghun Kim, E. James Whitehead Jr., Yi Zhang 0001
ML
2010
ACM
210views Machine Learning» more  ML 2010»
13 years 7 months ago
Mining frequent closed rooted trees
Many knowledge representation mechanisms are based on tree-like structures, thus symbolizing the fact that certain pieces of information are related in one sense or another. There ...
José L. Balcázar, Albert Bifet, Anto...