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Journal Article•DOI•

Pattern Recognition and Machine Learning

Radford M. Neal
01 Aug 2007-Technometrics (Taylor & Francis)-Vol. 49, Iss: 3, pp 366-366
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.
Citations
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Journal Article•DOI•
Device Fingerprinting in Wireless Networks: Challenges and Opportunities

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Qiang Xu1, Rong Zheng1, Walid Saad2, Zhu Han3•
McMaster University1, Virginia Tech2, University of Houston3
21 Jan 2016-IEEE Communications Surveys and Tutorials
TL;DR: In this paper, the authors provide a comprehensive taxonomy of wireless features that can be used in fingerprinting, and provide a systematic review on fingerprint algorithms including both white-list based and unsupervised learning approaches.
Abstract: Node forgery or impersonation, in which legitimate cryptographic credentials are captured by an adversary, constitutes one major security threat facing wireless networks. The fact that mobile devices are prone to be compromised and reverse engineered significantly increases the risk of such attacks in which adversaries can obtain secret keys on trusted nodes and impersonate the legitimate node. One promising approach toward thwarting these attacks is through the extraction of unique fingerprints that can provide a reliable and robust means for device identification. These fingerprints can be extracted from transmitted signal by analyzing information across the protocol stack. In this paper, the first unified and comprehensive tutorial in the area of wireless device fingerprinting for security applications is presented. In particular, we aim to provide a detailed treatment on developing novel wireless security solutions using device fingerprinting techniques. The objectives are three-fold: (i) to introduce a comprehensive taxonomy of wireless features that can be used in fingerprinting, (ii) to provide a systematic review on fingerprint algorithms including both white-list based and unsupervised learning approaches, and (iii) to identify key open research problems in the area of device fingerprinting and feature extraction, as applied to wireless security.

281 citations

Journal Article•DOI•
Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping

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Andrii Shelestov1, Mykola Lavreniuk1, Nataliia Kussul1, Alexei Novikov1, Sergii Skakun2, Sergii Skakun3 •
National Technical University1, Goddard Space Flight Center2, University of Maryland, College Park3
24 Feb 2017-Frontiers in Earth Science
TL;DR: Efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale and in terms of classification accuracy, the neural network based approach outperformed support vector machine, decision tree and random forest classifiers available in GEE.
Abstract: Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory (~28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.

280 citations

Cites background or methods from "Pattern Recognition and Machine Lea..."

  • ...Within training cross-entropy (CE) error function is minimized (Bishop, 2006) E(w) = − ln p(T|w) = − N∑ n= 1 K∑ k= 1 tnk ln ynk (1) where E(w) is the CE error function that depends on the neurons’ weight coefficients w, T is the set of vectors of target outputs in the training set composed of N…...

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  • ...One disadvantage of the DT classifier is the considerable sensitivity to the training dataset, so that a small change to the training data can result in a very different set of subsets (Bishop, 2006)....

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  • ...An important property of SVMs is that the determination of the model parameters corresponds to a convex optimization problem, and so any local solution is also a global optimum (Bishop, 2006)....

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  • ...The main difference from other models and algorithms is the outcome score that could be considered as a probability value (Bishop, 2006; Haykin, 2008)....

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  • ...Within training cross-entropy (CE) error function is minimized (Bishop, 2006)...

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Journal Article•DOI•
A survey of MRI-based brain tumor segmentation methods

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Jin Liu1, Min Li1, Jianxin Wang1, Fang-Xiang Wu2, Tianming Liu3, Yi Pan4 •
Central South University1, University of Saskatchewan2, University of Georgia3, Georgia State University4
20 Nov 2014-Tsinghua Science & Technology
TL;DR: The preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced, the evaluation and validation of the results are discussed, and an objective assessment is presented.

279 citations

Cites background from "Pattern Recognition and Machine Lea..."

  • ...It can potentially reduce the burden on radiologists in the practice of radiology[57], which can learn complex relationships or patterns from empirical data and make accurate decisions[58]....

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Journal Article•DOI•
Scalable Variational Inference for Bayesian Variable Selection in Regression, and Its Accuracy in Genetic Association Studies

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Peter Carbonetto1, Matthew Stephens1•
University of Chicago1
01 Mar 2012-Bayesian Analysis
TL;DR: This work assesses an alternative to MCMC based on a simple variational approximation to retain useful features of Bayesian variable selection at a reduced cost and illustrates how these results guide the use of variational inference for a genome-wide association study with thousands of samples and hundreds of thousands of variables.
Abstract: The Bayesian approach to variable selection in regression is a powerful tool for tackling many scientific problems. Inference for variable selection models is usually implemented using Markov chain Monte Carlo (MCMC). Because MCMC can impose a high computational cost in studies with a large number of variables, we assess an alternative to MCMC based on a simple variational approximation. Our aim is to retain useful features of Bayesian variable selection at a reduced cost. Using simulations designed to mimic genetic association studies, we show that this simple variational approximation yields posterior inferences in some settings that closely match exact values. In less restrictive (and more realistic) conditions, we show that posterior probabilities of inclusion for individual variables are often incorrect, but variational estimates of other useful quantities|including posterior distributions of the hyperparameters|are remarkably accurate. We illustrate how these results guide the use of variational inference for a genome-wide association study with thousands of samples and hundreds of thousands of variables.

279 citations

Additional excerpts

  • ...Skipping the derivation (see Bishop 2006 or Jaakkola and Jordan 2000 for details), the lower bound on the logarithm of the sigmoid function is logψ(x) ≥ logψ(η) + 12 (x− η) − u2 (x 2 − η2), (17) where we have defined u = 1η (ψ(η) − 12 )....

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Journal Article•DOI•
Comparing Dynamic Causal Models using AIC, BIC and Free Energy

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William D. Penny1•
Wellcome Trust Centre for Neuroimaging1
02 Jan 2012-NeuroImage
TL;DR: A simulation study which compares the relative merits of three model selection criteria finds that the Free Energy has the best model selection ability and recommends it be used for comparison of DCMs.

279 citations

Cites background or methods from "Pattern Recognition and Machine Lea..."

  • ...The posterior distribution is analytic and given by (Bishop, 2006) S−1θ = X TC−1y X+C −1 θ mθ = Sθ X TC−1y y+C −1 θ μθ ð27Þ Please cite this article as: Penny, W.D., Comparing Dynamic Causal Mode j.neuroimage.2011.07.039 These parameter values can then be plugged into Eqs....

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  • ...This is equivalent to the statement that BIC is equal to the Free Energy under the infinite data limit, and when the priors over parameters are flat, and the variational posterior is exact (see section 2.3 in (Attias, 1999) and page 217 in (Bishop, 2006))....

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  • ...1053-8119/$ – see front matter © 2011 Elsevier Inc. Al doi:10.1016/j.neuroimage.2011.07.039 Please cite this article as: Penny, W.D., Com j.neuroimage.2011.07.039 a b s t r a c t a r t i c l e i n f o...

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  • ...A generic approach for statistical inference in this context is Bayesian estimation (Bishop, 2006; Gelman et al., 1995) which provides estimates of two quantities....

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