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In this work, a homemade E-nose system embedded with 16 metal-oxide semi-conductive gas sensors was used to discriminate nine kinds of ginsengs of different species or production places.


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A flexible machine learning framework, Venn machine VM was introduced to make probabilistic predictions for each prediction. Three Venn predictors and three classical probabilistic prediction methods were compared in aspect of classification rate and especially the validity of estimated probability.

A best classification rate of The validity of Venn predictors performed better than that of corresponding classical probabilistic prediction methods. The results demonstrated that Venn machine is a flexible tool to make precise and valid probabilistic prediction in the application of E-nose, and VM-SVM achieved the best performance for the probabilistic prediction of ginseng samples. This paper proposes a new approach to improving our understanding of the suitability of internet discussion forums for use by health information seekers.

We consider in particular their potential use during public health emergencies when access to conventional experts and healthcare professionals may be constrained.

Statistical Inference-Meaning and Importance - Population and Sample - Data and Data Collection

We explore potential benefits and challenges of crowdsourcing information on health issues in online environments through the context of Computer Science theories of Collective Intelligence [1, 2], which explore how members of a group - particularly when networked by computer systems - can reach a better solution than an individual working alone. We study optimal conformity measures for various criteria of efficiency in an idealised setting.

This leads to an important class of criteria of efficiency that we call probabilistic; it turns out that the most standard criteria of efficiency used in literature on conformal prediction are not probabilistic. We construct a universal prediction system in the spirit of Popper's falsifiability and Kolmogorov complexity. This prediction system does not depend on any statistical assumptions, but under the IID assumption it dominates, although in a rather weak sense, conformal prediction. Despite the extensive attention on traditional stability of data perturbations or parameter variations, few studies include influences coming from the intrinsic randomness in generating VIMs, i.

To address these influences, in this paper we introduce a new concept of intrinsic stability of VIMs, which is defined as the self-consistence among feature rankings in repeated runs of VIMs without data perturbations and parameter variations. Two widely used VIMs, i. The motivation of this study is two-fold. First, we empirically verify the prevalence of intrinsic stability of VIMs over many real-world datasets to highlight that the instability of VIMs does not originate exclusively from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs.

Second, through Spearman and Pearson tests we comprehensively investigate how different factors influence the intrinsic stability. This paper reports a hybrid system consisting of a homemade electronic nose system E-nose with a sensor array of 16 metal-oxide sensors and a near-infrared reflectance spectroscopy NIRS system for discriminating different kinds of ginsengs. Six commonly used features were extracted from each sensor in the E-nose sensor array. Then, models were built and trained with a support vector machine separately using datasets of the two systems.

The classification performances of individual systems were optimized and compared. The advantages and disadvantages of the two systems were demonstrated by comparing empirical probability distributions in the category of predict labels for all samples. Finally, new weighted feature-level data-fusion and Dempster—Shafer-theory based decision-level data-fusion approaches for the hybrid system were separately exploited. The results showed that the hybrid system achieved an optimal classification accuracy of Sort by year:.

Summary Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Summary Data analysis and inference have traditionally been research areas of statistics. Summary This book is devoted to two interrelated techniques in solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. Summary The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction.

Summary This book celebrates the work of Vladimir Vapnik, developer of the support vector machine, which combines methods from statistical learning and functional analysis to create a new approach to learning problems, and who continues as active as ever in his field. Summary This book brings together historical notes, reviews of research developments, fresh ideas on how to make VC Vapnik—Chervonenkis guarantees tighter, and new technical contributions in the areas of machine learning, statistical inference, classification, algorithmic statistics, and pattern recognition.

Estimation Of Dependences Based On Empirical Data Empirical Inference Science

Summary The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. Summary A new game—theoretic approach to probability and finance. Summary A comprehensive look at learning and generalization theory. Paper Proceedings of Machine Learning Research.

A systems biology analysis of brain microvascular endothelial cell lipotoxicity

Abstract Conformal predictive systems are a recent modification of conformal predictors that output, in regression problems, probability distributions for labels of test observations rather than set predictions. Abstract This note explains how conformal predictive distributions can be used for the purpose of decision-making. Abstract Venn predictors are a distribution-free probabilistic prediction framework that transforms the output of a scoring classifier into a multi- probabilistic prediction that has calibration guarantees, with the only requirement of an i.

Abstract This study examines the use of the Conformal Prediction CP framework for the provision of confidence information in the detection of seizures in electroencephalograph EEG recordings. Abstract Cough and sneeze are the most common means to spread respiratory diseases amongst humans. Abstract We consider the problem of feature selection for unsupervised anomaly detection AD in time-series, where only normal examples are available for training.

Journal Sensors, Vol. Abstract An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention.

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Journal Annals of Mathematics and Artificial Intelligence. Abstract The paper presents an application of Conformal Predictors to a chemoinformatics problem of predicting the biological activities of chemical compounds. Abstract The paper presents some possible approaches to the combination of Conformal Predictors in the binary classification case.

Journal Finance and Stochastics. Abstract This paper argues that the requirement of measurability imposed on trading strategies is indispensable in continuous-time game-theoretic probability. Abstract We consider the problem of quickest change-point detection in data streams. Abstract We construct universal prediction systems in the spirit of Popper's falsifiability and Kolmogorov complexity and randomness.

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Abstract We study optimal conformity measures for various criteria of efficiency of set-valued classification in an idealised setting. Journal Measurement Science and Technology, Vol. Abstract A novel disposable all-solid-state carbonate-selective electrode based on a screen-printed carbon paste electrode using poly 3-octylthiophene-2,5-diyl POT as an ion-to-electron transducer has been developed. Abstract This paper applies conformal prediction to derive predictive distributions that are valid under a nonparametric assumption.


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  • Vienna, Austria: ACM, p. Abstract Malware evolves perpetually and relies on increasingly so- phisticated attacks to supersede defense strategies. Abstract Learning with expert advice as a scheme of on-line learning has been very successfully applied to various learning problems due to its strong theoretical basis. Abstract The paper presents a competitive prediction-style upper bound on the square loss of the Aggregating Algorithm for Regression with Changing Dependencies in the linear case. Abstract In the application of electronic noses E-noses , probabilistic prediction is a good way to estimate how confident we are about our prediction.

    Abstract This paper proposes a new approach to improving our understanding of the suitability of internet discussion forums for use by health information seekers. Abstract We study optimal conformity measures for various criteria of efficiency in an idealised setting. Abstract We construct a universal prediction system in the spirit of Popper's falsifiability and Kolmogorov complexity.

    Abstract This paper reports a hybrid system consisting of a homemade electronic nose system E-nose with a sensor array of 16 metal-oxide sensors and a near-infrared reflectance spectroscopy NIRS system for discriminating different kinds of ginsengs. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. Probabilistic Conditional Independence Structures provides the mathematical description of probabilistic conditional independence structures; the author uses non-graphical methods of their description, and takes an algebraic appr.

    This book is a comprehensive and accessible introduction to the cross-entropy CE method. The CE method started life around when the first author proposed an adaptive algorithm for rare-event simulation using a cross-entropy minimization …. Monte Carlo methods are revolutionising the on-line analysis of data in fields as diverse as financial modelling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo ….

    In the fall of , I was asked to teach a course on computer intrusion detection for the Department of Mathematical Sciences of The Johns Hopkins University. That course was the genesis of this book. I had been working in the field for several …. Bayesian networks and decision graphs are formal graphical languages for representation and communication of decision scenarios requiring reasoning under uncertainty. Their strengths are two-sided.

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    It is easy for humans to construct and to …. The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Geiger, P. Locatello, F. Suter, R.

    Bibliographic Information

    Simon-Gabriel, C. Raj, A. Aghaeifar, A. A channel multi-coil setup optimized for human brain shimming at 9. Tabibian, B. Mehrjou, A. Meding, K. Runge, J. Inferring causation from time series with perspectives in Earth system sciences Nature Communications , article In revision [BibTex]. Lim, J. Hohmann, M. Bruijns, S. Abstract Variational Autoencoders VAEs provide a theoretically-backed framework for deep generative models. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder.

    In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior.

    To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs.