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ACML
2009
Springer
4 years 2 months ago
Max-margin Multiple-Instance Learning via Semidefinite Programming
In this paper, we present a novel semidefinite programming approach for multiple-instance learning. We first formulate the multipleinstance learning as a combinatorial maximum marg...
Yuhong Guo
ACML
2009
Springer
4 years 2 months ago
Learning Algorithms for Domain Adaptation
A fundamental assumption for any machine learning task is to have training and test data instances drawn from the same distribution while having a sufficiently large number of tra...
Manas A. Pathak, Eric Nyberg
ACML
2009
Springer
4 years 2 months ago
Improving Adaptive Bagging Methods for Evolving Data Streams
We propose two new improvements for bagging methods on evolving data streams. Recently, two new variants of Bagging were proposed: ADWIN Bagging and Adaptive-Size Hoeffding Tree (...
Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer...
ACML
2009
Springer
4 years 4 months ago
Injecting Structured Data to Generative Topic Model in Enterprise Settings
Enterprises have accumulated both structured and unstructured data steadily as computing resources improve. However, previous research on enterprise data mining often treats these ...
Han Xiao, Xiaojie Wang, Chao Du
ACML
2009
Springer
4 years 4 months ago
Conditional Density Estimation with Class Probability Estimators
Many regression schemes deliver a point estimate only, but often it is useful or even essential to quantify the uncertainty inherent in a prediction. If a conditional density estim...
Eibe Frank, Remco R. Bouckaert
ACML
2009
Springer
4 years 4 months ago
Coupled Metric Learning for Face Recognition with Degraded Images
Real-world face recognition systems are sometimes confronted with degraded face images, e.g., low-resolution, blurred, and noisy ones. Traditional two-step methods have limited per...
Bo Li, Hong Chang, Shiguang Shan, Xilin Chen
ACML
2009
Springer
4 years 4 months ago
Linear Time Model Selection for Mixture of Heterogeneous Components
Abstract: Our main contribution is to propose a novel model selection methodology, expectation minimization of information criterion (EMIC). EMIC makes a significant impact on the...
Ryohei Fujimaki, Satoshi Morinaga, Michinari Momma...
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