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ICML
2009
IEEE
8 years 4 months ago
Multiple indefinite kernel learning with mixed norm regularization
We address the problem of learning classifiers using several kernel functions. On the contrary to many contributions in the field of learning from different sources of information...
Matthieu Kowalski, Marie Szafranski, Liva Ralaivol...
ICML
2009
IEEE
8 years 4 months ago
Accelerated sampling for the Indian Buffet Process
We often seek to identify co-occurring hidden features in a set of observations. The Indian Buffet Process (IBP) provides a nonparametric prior on the features present in each obs...
Finale Doshi-Velez, Zoubin Ghahramani
ICML
2009
IEEE
8 years 4 months ago
Learning prediction suffix trees with Winnow
Nikolaos Karampatziakis, Dexter Kozen
ICML
2009
IEEE
9 years 1 months ago
Feature hashing for large scale multitask learning
Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bo...
Kilian Q. Weinberger, Anirban Dasgupta, John Langf...
ICML
2009
IEEE
9 years 1 months ago
Fast evolutionary maximum margin clustering
The maximum margin clustering approach is a recently proposed extension of the concept of support vector machines to the clustering problem. Briefly stated, it aims at finding a...
Fabian Gieseke, Tapio Pahikkala, Oliver Kramer
ICML
2009
IEEE
9 years 1 months ago
Sparse higher order conditional random fields for improved sequence labeling
In real sequence labeling tasks, statistics of many higher order features are not sufficient due to the training data sparseness, very few of them are useful. We describe Sparse H...
Xian Qian, Xiaoqian Jiang, Qi Zhang, Xuanjing Huan...
ICML
2009
IEEE
9 years 1 months ago
Rule learning with monotonicity constraints
In classification with monotonicity constraints, it is assumed that the class label should increase with increasing values on the attributes. In this paper we aim at formalizing ...
Wojciech Kotlowski, Roman Slowinski
ICML
2009
IEEE
9 years 1 months ago
Learning linear dynamical systems without sequence information
Virtually all methods of learning dynamic systems from data start from the same basic assumption: that the learning algorithm will be provided with a sequence, or trajectory, of d...
Tzu-Kuo Huang, Jeff Schneider
ICML
2009
IEEE
9 years 1 months ago
Decision tree and instance-based learning for label ranking
The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ra...
Weiwei Cheng, Jens C. Huhn, Eyke Hüllermeier
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