Markus Breitenbach

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Machine learning applications often involve data that can be analyzed as unit vectors on a d-dimensional hypersphere, or equivalently are directional in nature. Spectral clustering techniques generate embeddings that constitute an example of directional data and can result in different shapes on a hy-persphere (depending on the original structure). Other(More)
A new class of nonparametric algorithms for high-dimensional binary classification is proposed using cascades of low dimensional polynomial structures. Construction of polynomial cascades is based on Minimax Probability Machine Classification (MPMC), which results in direct estimates of classification accuracy, and provides a simple stopping criteria that(More)
—802.11 localization algorithms provide the ability to accurately position and track wireless clients thereby enabling location-based services and applications. However, we show that these localization techniques are vulnerable to non-cryptographic attacks where an adversary uses a low-cost directional antenna to appear from the localization algorithm's(More)
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