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» Evaluating algorithms that learn from data streams
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ICASSP
2010
IEEE
15 years 6 months ago
Semi-Supervised Fisher Linear Discriminant (SFLD)
Supervised learning uses a training set of labeled examples to compute a classifier which is a mapping from feature vectors to class labels. The success of a learning algorithm i...
Seda Remus, Carlo Tomasi
DMIN
2010
262views Data Mining» more  DMIN 2010»
15 years 4 months ago
SMO-Style Algorithms for Learning Using Privileged Information
Recently Vapnik et al. [11, 12, 13] introduced a new learning model, called Learning Using Privileged Information (LUPI). In this model, along with standard training data, the tea...
Dmitry Pechyony, Rauf Izmailov, Akshay Vashist, Vl...
PET
2005
Springer
15 years 12 months ago
Privacy Vulnerabilities in Encrypted HTTP Streams
Abstract. Encrypting traffic does not prevent an attacker from performing some types of traffic analysis. We present a straightforward traffic analysis attack against encrypted HT...
George Dean Bissias, Marc Liberatore, David Jensen...
ICMI
2003
Springer
96views Biometrics» more  ICMI 2003»
15 years 11 months ago
Learning and reasoning about interruption
We present methods for inferring the cost of interrupting users based on multiple streams of events including information generated by interactions with computing devices, visual ...
Eric Horvitz, Johnson Apacible
NCA
2002
IEEE
15 years 6 months ago
Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks
Fitting gaussian peaks to experimental data is important in many disciplines, including nuclear spectroscopy. Nonlinear least squares fitting methods have been in use for a long t...
Radwan E. Abdel-Aal