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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•
Accurate interatomic force fields via machine learning with covariant kernels

[...]

Aldo Glielmo1, Peter Sollich1, Alessandro De Vita2, Alessandro De Vita1•
King's College London1, University of Trieste2
08 Jun 2017-Physical Review B
TL;DR: A novel scheme to accurately predict atomic forces as vector quantities, rather than sets of scalar components, by Gaussian process (GP) regression is presented, based on matrix-valued kernel functions.
Abstract: We present a novel scheme to accurately predict atomic forces as vector quantities, rather than sets of scalar components, by Gaussian process (GP) regression. This is based on matrix-valued kernel functions, on which we impose the requirements that the predicted force rotates with the target configuration and is independent of any rotations applied to the configuration database entries. We show that such covariant GP kernels can be obtained by integration over the elements of the rotation group $\mathit{SO}(d)$ for the relevant dimensionality $d$. Remarkably, in specific cases the integration can be carried out analytically and yields a conservative force field that can be recast into a pair interaction form. Finally, we show that restricting the integration to a summation over the elements of a finite point group relevant to the target system is sufficient to recover an accurate GP. The accuracy of our kernels in predicting quantum-mechanical forces in real materials is investigated by tests on pure and defective Ni, Fe, and Si crystalline systems.

230 citations

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

  • ...If the likelihood function [24] is also Gaussian (which effectively assumes that the observed forces f i are the true forces subject to Gaussian noise of variance σ 2 n ) then the resulting posterior distribution f(ρ|D), conditional on the data, will also be a Gaussian process f(ρ|D) ∼ GP(f̂(ρ|D),Ĉ(ρ,ρ ′))....

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  • ...It is easy to check that standard kernels such as the squared exponential [24] or the overlap integral of atomic configuration [35] do not possess the covariance property (7)....

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  • ..., Gaussian process (GP) regression [22,23] or neural networks [24]....

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Journal Article•DOI•
Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images

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Marek Kowal1, Paweł Filipczuk1, Andrzej Obuchowicz1, Józef Korbicz1, Roman Monczak •
University of Zielona Góra1
01 Oct 2013-Computers in Biology and Medicine
TL;DR: Four different clustering algorithms are tested and compared in the task of fast nuclei segmentation and it is shown that the presented method ensures accurate and objective data acquisition that could be used to facilitate breast cancer diagnosis.

230 citations

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

  • ...The classification is performed with 3 different classification approaches: k-nearest neighbors (k-NN) [35] for k = 7 chosen experimentally, naive Bayes classifier with kernel density estimate [36, 37, 38], and decision trees [39]....

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Journal Article•DOI•
Deliberation for autonomous robots: A survey

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Félix Ingrand, Malik Ghallab
01 Jun 2017-Artificial Intelligence
TL;DR: A global overview of deliberation functions in robotics is presented and the main characteristics, design choices and constraints of these functions are discussed.

229 citations

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

  • ...For that, one may draw from the wealth of pattern recognition techniques in machine learning [23], and from the growing set of labeled data and corresponding models on the web....

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Proceedings Article•DOI•
Query dependent pseudo-relevance feedback based on wikipedia

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Yang Xu1, Gareth J. F. Jones2, Bin Wang1•
Chinese Academy of Sciences1, Dublin City University2
19 Jul 2009
TL;DR: This work proposes and proposes and studies the effectiveness of three methods for expansion term selection, each modeling the Wikipedia based pseudo-relevance information from a different perspective, and incorporates the expansion terms into the original query and uses language modeling IR to evaluate these methods.
Abstract: Pseudo-relevance feedback (PRF) via query-expansion has been proven to be e®ective in many information retrieval (IR) tasks. In most existing work, the top-ranked documents from an initial search are assumed to be relevant and used for PRF. One problem with this approach is that one or more of the top retrieved documents may be non-relevant, which can introduce noise into the feedback process. Besides, existing methods generally do not take into account the significantly different types of queries that are often entered into an IR system. Intuitively, Wikipedia can be seen as a large, manually edited document collection which could be exploited to improve document retrieval effectiveness within PRF. It is not obvious how we might best utilize information from Wikipedia in PRF, and to date, the potential of Wikipedia for this task has been largely unexplored. In our work, we present a systematic exploration of the utilization of Wikipedia in PRF for query dependent expansion. Specifically, we classify TREC topics into three categories based on Wikipedia: 1) entity queries, 2) ambiguous queries, and 3) broader queries. We propose and study the effectiveness of three methods for expansion term selection, each modeling the Wikipedia based pseudo-relevance information from a different perspective. We incorporate the expansion terms into the original query and use language modeling IR to evaluate these methods. Experiments on four TREC test collections, including the large web collection GOV2, show that retrieval performance of each type of query can be improved. In addition, we demonstrate that the proposed method out-performs the baseline relevance model in terms of precision and robustness.

229 citations

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

  • ...We use Support Vector Machines (SVMs) [1], which are a popular supervised learner for tasks such as this, as a classifier....

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Journal Article•DOI•
An Optical Communication's Perspective on Machine Learning and Its Applications

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Faisal Nadeem Khan1, Qirui Fan1, Chao Lu1, Alan Pak Tao Lau1•
Hong Kong Polytechnic University1
15 Jan 2019-Journal of Lightwave Technology
TL;DR: The mathematical foundations of basic ML techniques from communication theory and signal processing perspectives are described, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use.
Abstract: Machine learning (ML) has disrupted a wide range of science and engineering disciplines in recent years. ML applications in optical communications and networking are also gaining more attention, particularly in the areas of nonlinear transmission systems, optical performance monitoring, and cross-layer network optimizations for software-defined networks. However, the extent to which ML techniques can benefit optical communications and networking is not clear and this is partly due to an insufficient understanding of the nature of ML concepts. This paper aims to describe the mathematical foundations of basic ML techniques from communication theory and signal processing perspectives, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use. This will be followed by an overview of ongoing ML research in optical communications and networking with a focus on physical layer issues.

228 citations

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

  • ..., classification problem [3], which is more commonly referred to as clustering problem in ML literature....

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  • ...Results after (b) first E step; (c) first M step; (d) 2 complete EM iterations; (e) 5 complete EM iteratons; and (f) 20 complete EM iterations [3]....

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  • ...The choice of a kernel function is often determined by the designer’s knowledge of the problem domain [3]....

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  • ...The EM algorithm is a two-step iterative procedure comprising of expectation (E) and maximization (M) steps [3]....

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