A Weakly Supervised Learning Approach based on Spectral Graph-Theoretic Grouping

no code implementations • 3 Aug 2015 • Tameem Adel, Alexander Wong, Daniel Stashuk

In this study, a spectral graph-theoretic grouping strategy for weakly supervised classification is introduced, where a limited number of labelled samples and a larger set of unlabelled samples are used to construct a larger annotated training set composed of strongly labelled and weakly labelled samples.

Classification General Classification +2

Affine and Regional Dynamic Time Warpng

no code implementations • 25 May 2015 • Tsu-Wei Chen, Meena Abdelmaseeh, Daniel Stashuk

There are situations where time series alignment should be invariant to scaling and offset in amplitude or where local regions of the considered time series should be strongly reflected in pointwise matches.

Dynamic Time Warping Time Series +1

Generative Multiple-Instance Learning Models For Quantitative Electromyography

no code implementations • 26 Sep 2013 • Tameem Adel, Benn Smith, Ruth Urner, Daniel Stashuk, Daniel J. Lizotte

We present a comprehensive study of the use of generative modeling approaches for Multiple-Instance Learning (MIL) problems.

Multiple Instance Learning