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» Detecting worm variants using machine learning
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99
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ICML
2002
IEEE
16 years 3 months ago
Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approach
We present two results which arise from a model-based approach to hierarchical agglomerative clustering. First, we show formally that the common heuristic agglomerative clustering...
Sepandar D. Kamvar, Dan Klein, Christopher D. Mann...
130
Voted
ICDM
2009
IEEE
160views Data Mining» more  ICDM 2009»
15 years 9 months ago
Fast Online Training of Ramp Loss Support Vector Machines
—A fast online algorithm OnlineSVMR for training Ramp-Loss Support Vector Machines (SVMR s) is proposed. It finds the optimal SVMR for t+1 training examples using SVMR built on t...
Zhuang Wang, Slobodan Vucetic
ACML
2009
Springer
15 years 9 months ago
Estimating Likelihoods for Topic Models
Abstract. Topic models are a discrete analogue to principle component analysis and independent component analysis that model topic at the word level within a document. They have ma...
Wray L. Buntine
150
Voted
WMCSA
2008
IEEE
15 years 9 months ago
HealthSense: classification of health-related sensor data through user-assisted machine learning
Remote patient monitoring generates much more data than healthcare professionals are able to manually interpret. Automated detection of events of interest is therefore critical so...
Erich P. Stuntebeck, John S. Davis II, Gregory D. ...
BMCBI
2006
216views more  BMCBI 2006»
15 years 2 months ago
Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data
Background: Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfu...
Haiying Wang, Huiru Zheng, David Simpson, Francisc...