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ML
2010
ACM
159views Machine Learning» more  ML 2010»
13 years 7 months ago
Algorithms for optimal dyadic decision trees
Abstract A dynamic programming algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data ...
Don R. Hush, Reid B. Porter
ML
2010
ACM
125views Machine Learning» more  ML 2010»
13 years 7 months ago
A process for predicting manhole events in Manhattan
Cynthia Rudin, Rebecca J. Passonneau, Axinia Radev...
ML
2010
ACM
13 years 7 months ago
Temporal kernel CCA and its application in multimodal neuronal data analysis
Felix Bießmann, Frank C. Meinecke, Arthur Gr...
ML
2010
ACM
181views Machine Learning» more  ML 2010»
13 years 7 months ago
Decomposing the tensor kernel support vector machine for neuroscience data with structured labels
Abstract The tensor kernel has been used across the machine learning literature for a number of purposes and applications, due to its ability to incorporate samples from multiple s...
David R. Hardoon, John Shawe-Taylor
ML
2010
ACM
142views Machine Learning» more  ML 2010»
13 years 7 months ago
Fast adaptive algorithms for abrupt change detection
We propose two fast algorithms for abrupt change detection in streaming data that can operate on arbitrary unknown data distributions before and after the change. The first algor...
Daniel Nikovski, Ankur Jain
ML
2010
ACM
151views Machine Learning» more  ML 2010»
13 years 7 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
ML
2010
ACM
13 years 7 months ago
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex K...
ML
2010
ACM
13 years 7 months ago
Semi-supervised local Fisher discriminant analysis for dimensionality reduction
When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled samples ...
Masashi Sugiyama, Tsuyoshi Idé, Shinichi Na...