Markus Breitenbach

Learn 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)
Recently proposed classification algorithms give estimates or worst-case bounds for the probability of misclassification [Lanckriet et al., 2002][L. Breiman, 2001]. These accuracy estimates are for all future predictions, even though some predictions are more likely to be correct than others. This paper introduces Probabilistic Random Forests (PRF), which(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)
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)
The recently proposed Polynomial MPMC Cascade (PMC) algorithm is a nonparametric classifier for high-dimensional non-linear binary classification with performance competitive with state-of-the-art classifiers like SVMs. Importantly, the algorithm has linear-time complexity with respect to both training-set size and dimensionality of the problem. In this(More)
Clustering aims at finding hidden structure in data. In this paper we present a new clustering algorithm that builds upon the local and global consistency method (Zhou, et.al., 2003), a semi-supervised learning technique with the property of learning very smooth functions with respect to the intrinsic structure revealed by the data. Starting from this(More)
Recently proposed classification algorithms give estimates or worst-case bounds for the probability of misclassification [Lanckriet et al., 2002][L. Breiman, 2001]. These accuracy estimates are for all future predictions, even though some predictions are more likely to be correct than others. This paper introduces Probabilistic Random Forests (PRF), which(More)