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Journal ArticleDOI

Pattern Recognition and Machine Learning

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.
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Journal ArticleDOI
TL;DR: In this paper, a new approach is discussed for the extraction of significant anomalous geochemical signature of the mineral deposit type sought and for assigning weights to anomaly classes in a geochemical evidence map.
Abstract: Stream sediment geochemical data are usually subjected to methods of multivariate analysis (e.g. factor analysis) in order to extract an anomalous geochemical signature (factor) of the mineral deposit-type sought. A map of anomalous geochemical signature can be used as evidence, in combination with other layers of evidence, for mineral prospectivity mapping (MPM). Because factor analysis may yield more than one factor in a stream sediment dataset, it raises the challenge of how to recognize the factor that best indicates presence of the mineral deposit-type sought. In addition, MPM is faced with the challenge of how to assign weights to classes in a geochemical evidence map. Accordingly, a new approach is discussed in this paper for the extraction of significant anomalous geochemical signature of the mineral deposit-type sought and for assigning weights to anomaly classes in a geochemical evidence map. In this approach, we used a staged factor analysis and then applied a logistic function to transform factor scores representing an anomalous geochemical signature in order to derive a map of geochemical mineralisation prospectivity indices (GMPI) as a spatial evidence layer for MPM based on the theory of fuzzy sets and fuzzy logic. The GMPI is a fuzzy weight in the [0,1] range. We demonstrate the application of the GMPI for mapping prospectivity for Mississippi valley-type fluorite deposits in the Mazandaran province, north of Iran, which is a greenfield area.

134 citations


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

  • ...Bishop (2006) has illustrated that transformed variables using a logistic sigmoid function gains more optimal decision boundary for classification compared to non-transformed variables....

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  • ...To assign weights to classes of spatial evidence in order to create predictor maps, either knowledgeor data-driven methods for MPM are used (Bonham-Carter 1994; Carranza 2008). With knowledge-driven methods, which are appropriate in greenfield (or poorly explored) areas, subjective judgment of an expert analyst is employed in assigning weights to classes of a spatial evidence layer. The theory of fuzzy sets and fuzzy logic (Zadeh 1965) has been successfully applied in knowledge-driven MPM. In fuzzy logic MPM, the fuzzy weights assigned to spatial evidence must reflect realistic spatial associations between spatial evidence and mineral deposits of the type sought. Fuzzification, or assignment of fuzzy weights, is the most important stage in fuzzy logic MPM (Carranza 2008). Recent examples of fuzzy logic MPM are found in D’Ercole et al. (2000); Knox-Robinson (2000); Porwal & Sides (2000); Venkataraman et al. (2000); Carranza & Hale (2001); Porwal et al. (2003, 2004, 2006), Tangestani & Moore (2003); Ranjbar & Honarmand (2004); Eddy et al....

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  • ...To assign weights to classes of spatial evidence in order to create predictor maps, either knowledgeor data-driven methods for MPM are used (Bonham-Carter 1994; Carranza 2008). With knowledge-driven methods, which are appropriate in greenfield (or poorly explored) areas, subjective judgment of an expert analyst is employed in assigning weights to classes of a spatial evidence layer. The theory of fuzzy sets and fuzzy logic (Zadeh 1965) has been successfully applied in knowledge-driven MPM. In fuzzy logic MPM, the fuzzy weights assigned to spatial evidence must reflect realistic spatial associations between spatial evidence and mineral deposits of the type sought. Fuzzification, or assignment of fuzzy weights, is the most important stage in fuzzy logic MPM (Carranza 2008). Recent examples of fuzzy logic MPM are found in D’Ercole et al. (2000); Knox-Robinson (2000); Porwal & Sides (2000); Venkataraman et al. (2000); Carranza & Hale (2001); Porwal et al. (2003, 2004, 2006), Tangestani & Moore (2003); Ranjbar & Honarmand (2004); Eddy et al. (2006); Rogge et al....

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  • ...Following Bishop (2006), we have provided here Figure 5 for further illustration of the better performance of logistic transformation (non-linear) compared to linear transformation to discriminate between background and anomaly....

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  • ...Non-linear transformation gains an optimal decision boundary between different classes of a variable for classification purposes (Bishop 2006)....

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Journal ArticleDOI
TL;DR: In this paper, Score-Matching by Denoising (SMD) is proposed to match a score (i.e., the gradient of a log-prior).
Abstract: Regularization by denoising (RED), as recently proposed by Romano, Elad, and Milanfar, is powerful image-recovery framework that aims to minimize an explicit regularization objective constructed from a plug-in image-denoising function. Experimental evidence suggests that the RED algorithms are a state of the art. We claim, however, that explicit regularization does not explain the RED algorithms. In particular, we show that many of the expressions in the paper by Romano et al. hold only when the denoiser has a symmetric Jacobian, and we demonstrate that such symmetry does not occur with practical denoisers such as nonlocal means, BM3D, TNRD, and DnCNN. To explain the RED algorithms, we propose a new framework called Score-Matching by Denoising (SMD), which aims to match a “score” (i.e., the gradient of a log-prior). We then show tight connections between SMD, kernel density estimation, and constrained minimum mean-squared error denoising. Furthermore, we interpret the RED algorithms from Romano et al. and propose new algorithms with acceleration and convergence guarantees. Finally, we show that the RED algorithms seek a consensus equilibrium solution, which facilitates a comparison to plug-and-play ADMM.

134 citations

Journal ArticleDOI
TL;DR: A novel Bayesian algorithm is used to track dynamic transitions between hidden neural states in human brain activity and to relate brain dynamics with behavior, demonstrating the power of switching dynamical systems models for investigating hidden dynamic brain states and functional interactions underlying human cognition.
Abstract: Human cognition is influenced not only by external task demands but also latent mental processes and brain states that change over time. Here, we use novel Bayesian switching dynamical systems algorithm to identify hidden brain states and determine that these states are only weakly aligned with external task conditions. We compute state transition probabilities and demonstrate how dynamic transitions between hidden states allow flexible reconfiguration of functional brain circuits. Crucially, we identify latent transient brain states and dynamic functional circuits that are optimal for cognition and show that failure to engage these states in a timely manner is associated with poorer task performance and weaker decision-making dynamics. We replicate findings in a large sample (N = 122) and reveal a robust link between cognition and flexible latent brain state dynamics. Our study demonstrates the power of switching dynamical systems models for investigating hidden dynamic brain states and functional interactions underlying human cognition.

134 citations

Proceedings ArticleDOI
25 Oct 2008
TL;DR: Gaussian surface area essentially characterizes the computational complexity of learning under the Gaussian distribution, and this is the first subexponential time algorithm for learning general convex sets even in the noise-free (PAC) model.
Abstract: We study the learnability of sets in Ropfn under the Gaussian distribution, taking Gaussian surface area as the "complexity measure" of the sets being learned. Let CS denote the class of all (measurable) sets with surface area at most S. We first show that the class CS is learnable to any constant accuracy in time nO(S 2 ), even in the arbitrary noise ("agnostic'') model. Complementing this, we also show that any learning algorithm for CS information-theoretically requires 2Omega(S 2 ) examples for learning to constant accuracy. These results together show that Gaussian surface area essentially characterizes the computational complexity of learning under the Gaussian distribution. Our approach yields several new learning results, including the following (all bounds are for learning to any constant accuracy): The class of all convex sets can be agnostically learned in time 2O ~ (radicn) (and we prove a 2Omega(radicn) lower bound for noise-free learning). This is the first subexponential time algorithm for learning general convex sets even in the noise-free (PAC) model. Intersections of k halfspaces can be agnostically learned in time nO(log k) (cf. Vempala's nO(k) time algorithm for learning in the noise-free model).Cones (with apex centered at the origin), and spheres witharbitrary radius and center, can be agnostically learned in time poly(n).

133 citations


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

  • ...This is arguably the most natural distribution on Rn, especially from a machine learning perspective [8, 23, 32, 37]....

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Proceedings ArticleDOI
26 May 2019
TL;DR: This paper proposes an end-to-end deep learning framework, named DeepJIT, that automatically extracts features from commit messages and code changes and use them to identify defects.
Abstract: Software quality assurance efforts often focus on identifying defective code. To find likely defective code early, change-level defect prediction - aka. Just-In-Time (JIT) defect prediction - has been proposed. JIT defect prediction models identify likely defective changes and they are trained using machine learning techniques with the assumption that historical changes are similar to future ones. Most existing JIT defect prediction approaches make use of manually engineered features. Unlike those approaches, in this paper, we propose an end-to-end deep learning framework, named DeepJIT, that automatically extracts features from commit messages and code changes and use them to identify defects. Experiments on two popular software projects (i.e., QT and OPENSTACK) on three evaluation settings (i.e., cross-validation, short-period, and long-period) show that the best variant of DeepJIT (DeepJIT-Combined), compared with the best performing state-of-the-art approach, achieves improvements of 10.36--11.02% for the project QT and 9.51--13.69% for the project OPENSTACK in terms of the Area Under the Curve (AUC).

133 citations


Additional excerpts

  • ...The generated feature set, which is a nonlinear combination of the initial features, was put into a machine learning classifier [58] to predict buggy commits....

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