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NIPS
2000
15 years 5 months ago
High-temperature Expansions for Learning Models of Nonnegative Data
Recent work has exploited boundedness of data in the unsupervised learning of new types of generative model. For nonnegative data it was recently shown that the maximum-entropy ge...
Oliver B. Downs
ICPR
2004
IEEE
16 years 5 months ago
Signal Discrimination Using a Support Vector Machine for Genetic Syndrome Diagnosis
In this study, a support vector machine (SVM) classifies real world data of cytogenetic signals measured from fluorescence in-situ hybridization (FISH) images in order to diagnose...
Amit David, Boaz Lerner
FLAIRS
2003
15 years 5 months ago
Optimizing F-Measure with Support Vector Machines
Support vector machines (SVMs) are regularly used for classification of unbalanced data by weighting more heavily the error contribution from the rare class. This heuristic techn...
David R. Musicant, Vipin Kumar, Aysel Ozgur
SDM
2009
SIAM
202views Data Mining» more  SDM 2009»
16 years 1 months ago
Proximity-Based Anomaly Detection Using Sparse Structure Learning.
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...
Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe,...
ECML
2007
Springer
15 years 10 months ago
Learning from Relevant Tasks Only
We extend our recent work on relevant subtask learning, a new variant of multitask learning where the goal is to learn a good classifier for a task-of-interest with too few train...
Samuel Kaski, Jaakko Peltonen