Дисертації з теми "Kernel-based model"
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Bose, Aishwarya. "Effective web service discovery using a combination of a semantic model and a data mining technique." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/26425/1/Aishwarya_Bose_Thesis.pdf.
Повний текст джерелаBose, Aishwarya. "Effective web service discovery using a combination of a semantic model and a data mining technique." Queensland University of Technology, 2008. http://eprints.qut.edu.au/26425/.
Повний текст джерелаZhang, Lin. "Semiparametric Bayesian Kernel Survival Model for Highly Correlated High-Dimensional Data." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/95040.
Повний текст джерелаPHD
Garg, Aditie. "Designing Reactive Power Control Rules for Smart Inverters using Machine Learning." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83558.
Повний текст джерелаMaster of Science
Kim, Byung-Jun. "Semiparametric and Nonparametric Methods for Complex Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99155.
Повний текст джерелаDoctor of Philosophy
A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing the following three types of data: (1) matched case-crossover data, (2) HCHD data, and (3) Time-series data. We contribute to the development of statistical methods to deal with such complex data. First, under the matched study, we discuss an idea about hypothesis testing to effectively determine the association between observed factors and risk of interested disease. Because, in practice, we do not know the specific form of the association, it might be challenging to set a specific alternative hypothesis. By reflecting the reality, we consider the possibility that some observations are measured with errors. By considering these measurement errors, we develop a testing procedure under the matched case-crossover framework. This testing procedure has the flexibility to make inferences on various hypothesis settings. Second, we consider the data where the number of variables is very large compared to the sample size, and the variables are correlated to each other. In this case, our goal is to identify important variables for outcome among a large amount of the variables and build their network. For example, identifying few genes among whole genomics associated with diabetes can be used to develop biomarkers. By our proposed approach in the second project, we can identify differentially expressed and important genes and their network structure with consideration for the outcome. Lastly, we consider the scenario of changing patterns of interest over time with application to gas chromatography. We propose an efficient detection method to effectively distinguish the patterns of multi-level subjects in time-trend analysis. We suggest that our proposed method can give precious information on efficient search for the distinguishable patterns so as to reduce the burden of examining all observations in the data.
Polajnar, Tamara. "Semantic models as metrics for kernel-based interaction identification." Thesis, University of Glasgow, 2010. http://theses.gla.ac.uk/2260/.
Повний текст джерелаLyubchyk, Leonid, Oleksy Galuza, and Galina Grinberg. "Ranking Model Real-Time Adaptation via Preference Learning Based on Dynamic Clustering." Thesis, ННК "IПСА" НТУУ "КПI iм. Iгоря Сiкорського", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/36819.
Повний текст джерелаVlachos, Dimitrios. "Novel algorithms in wireless CDMA systems for estimation and kernel based equalization." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7658.
Повний текст джерелаBuch, Armin [Verfasser], and Gerhard [Akademischer Betreuer] Jäger. "Linguistic Spaces : Kernel-based models of natural language / Armin Buch ; Betreuer: Gerhard Jäger." Tübingen : Universitätsbibliothek Tübingen, 2011. http://d-nb.info/1161803572/34.
Повний текст джерелаMahfouz, Sandy. "Kernel-based machine learning for tracking and environmental monitoring in wireless sensor networkds." Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0025/document.
Повний текст джерелаThis thesis focuses on the problems of localization and gas field monitoring using wireless sensor networks. First, we focus on the geolocalization of sensors and target tracking. Using the powers of the signals exchanged between sensors, we propose a localization method combining radio-location fingerprinting and kernel methods from statistical machine learning. Based on this localization method, we develop a target tracking method that enhances the estimated position of the target by combining it to acceleration information using the Kalman filter. We also provide a semi-parametric model that estimates the distances separating sensors based on the powers of the signals exchanged between them. This semi-parametric model is a combination of the well-known log-distance propagation model with a non-linear fluctuation term estimated within the framework of kernel methods. The target's position is estimated by incorporating acceleration information to the distances separating the target from the sensors, using either the Kalman filter or the particle filter. In another context, we study gas diffusions in wireless sensor networks, using also machine learning. We propose a method that allows the detection of multiple gas diffusions based on concentration measures regularly collected from the studied region. The method estimates then the parameters of the multiple gas sources, including the sources' locations and their release rates
Zhai, Jing. "Efficient Exact Tests in Linear Mixed Models for Longitudinal Microbiome Studies." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/612412.
Повний текст джерелаFunke, Benedikt [Verfasser], Jeannette H. C. [Akademischer Betreuer] Woerner, and Herold [Gutachter] Dehling. "Kernel based nonparametric coefficient estimation in diffusion models / Benedikt Funke. Betreuer: Jeannette H. C. Woerner. Gutachter: Herold Dehling." Dortmund : Universitätsbibliothek Dortmund, 2015. http://d-nb.info/1111103275/34.
Повний текст джерелаStrengbom, Kristoffer. "Mobile Services Based Traffic Modeling." Thesis, Linköpings universitet, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-116459.
Повний текст джерелаAhmed, Mohamed Salem. "Contribution à la statistique spatiale et l'analyse de données fonctionnelles." Thesis, Lille 3, 2017. http://www.theses.fr/2017LIL30047/document.
Повний текст джерелаThis thesis is about statistical inference for spatial and/or functional data. Indeed, weare interested in estimation of unknown parameters of some models from random or nonrandom(stratified) samples composed of independent or spatially dependent variables.The specificity of the proposed methods lies in the fact that they take into considerationthe considered sample nature (stratified or spatial sample).We begin by studying data valued in a space of infinite dimension or so-called ”functionaldata”. First, we study a functional binary choice model explored in a case-controlor choice-based sample design context. The specificity of this study is that the proposedmethod takes into account the sampling scheme. We describe a conditional likelihoodfunction under the sampling distribution and a reduction of dimension strategy to definea feasible conditional maximum likelihood estimator of the model. Asymptotic propertiesof the proposed estimates as well as their application to simulated and real data are given.Secondly, we explore a functional linear autoregressive spatial model whose particularityis on the functional nature of the explanatory variable and the structure of the spatialdependence. The estimation procedure consists of reducing the infinite dimension of thefunctional variable and maximizing a quasi-likelihood function. We establish the consistencyand asymptotic normality of the estimator. The usefulness of the methodology isillustrated via simulations and an application to some real data.In the second part of the thesis, we address some estimation and prediction problemsof real random spatial variables. We start by generalizing the k-nearest neighbors method,namely k-NN, to predict a spatial process at non-observed locations using some covariates.The specificity of the proposed k-NN predictor lies in the fact that it is flexible and allowsa number of heterogeneity in the covariate. We establish the almost complete convergencewith rates of the spatial predictor whose performance is ensured by an application oversimulated and environmental data. In addition, we generalize the partially linear probitmodel of independent data to the spatial case. We use a linear process for disturbancesallowing various spatial dependencies and propose a semiparametric estimation approachbased on weighted likelihood and generalized method of moments methods. We establishthe consistency and asymptotic distribution of the proposed estimators and investigate thefinite sample performance of the estimators on simulated data. We end by an applicationof spatial binary choice models to identify UADT (Upper aerodigestive tract) cancer riskfactors in the north region of France which displays the highest rates of such cancerincidence and mortality of the country
Nguyen, Van Hanh. "Modèles de mélange semi-paramétriques et applications aux tests multiples." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00987035.
Повний текст джерелаJer-Fu, Liu, and 劉哲夫. "The Development of a Kernel for OODB Based on V-R Model." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/85060689060580327913.
Повний текст джерела國立交通大學
資訊科學學系
83
V-R模型(V-R model)是一個物件導向資料庫(OODB)的概念模型,此模 能 夠滿足物件導向資料庫的基本需求,而且對於物件的管理和查詢處 具有 一些良好的特性,因此我們採用此模型來發展OODB的核心系統。A者,由 於OODB的發展至目前為止,各項技術仍尚未完全成熟,因此希磏ォ悝畯怍 珛o展的系統不僅能縮短資料庫的開發時間,更能用來研發M測試各種適合 OODB的製作技術,例如查詢處理,物件儲存器等相關研s。由於V-R模型只 是一個概念模型,因此我們必須提出有效率的實作方k,並且設計一個完 善的系統架構,才能據此架構發展出我們所需的系峞C我們所發展的系統 允許物件身分的變換,因此在資料庫的使用上更膃蛣M性。而且使得物件 的資料更具一致性,所以可以減少物件修改的x難。同時此系統可以提供 良好的景象機制。最重要的是我們提供了一茯蒫oOODB的核心工具。最後 為了測試這個核心系統,我們利用此系統o展出一套物件導向資料庫系統 ,稱為EODBEasy use and efficient Object-oriented DataBase)。 We have developed a kernel for OODB in this thesis, basedn V-R model. V-R model, basically, is a conceptual model,hich can satisfy the fundamental requirements of the OODB.o it is natural and reasonable to develop our system,ased on the V-R model. By utilizing this kernel, we caneduce the research and development time for building aatabase system. Besides, we hope our kernel can also helpo test and develop those techniques necessary in an OODBystem, such as query processor, object storage, etc.ecause V-R model is only a conceptual model, we have toropose an effective way to implement it. Our system hashe following advantages: (1) extension of object*s role ,2) data integrity , (3) data sharing, and (4) a good viewechanism. Finally , we implement an OODB with our designedernel, named EODB(Easy use and efficient Object-orientedataBase) in our Database Lab. at Chiao Tung University.
Hao, Pei-Yi, and 郝沛毅. "Fuzzy Decision Model Using Support Vector Learning — A Kernel Function Based Approach." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/18494175702000813535.
Повний текст джерела國立成功大學
資訊工程學系碩博士班
91
Support Vector Machines (SVMs) have been recently introduced as a new technique for solving pattern recognition problems. According to the theory of SVMs, while traditional techniques for pattern recognition are based on the minimization of the empirical risk, that is, on the attempt to optimize the performance on the training set, SVMs minimize the structural risk, that is, the probability of misclassifying yet-to-been-seen patterns for a fixed but unknown probability distribution of the data. Fuzziness must be considered in systems where human estimation is influential. In this thesis, we incorporate the concept of fuzzy set theory into the support vector machine decision model in several approaches. We attempt to preserve the advantages of support vector machine (i.e. well generalization ability) and fuzzy set theory (i.e. closer to human thinking). First, we propose a fuzzy modeling framework based on support vector machine, a rule-based framework that explicitly characterizes the representation in fuzzy inference procedure. The support vector learning mechanism provides an architecture to extract support vectors for generating fuzzy IF-THEN rules from the training data set, and a method to describe the fuzzy system in terms of kernel functions. Thus, it has the inherent advantage that the model does not have to determine the number of rules in advance. Moreover, the decision model is not a black box anymore. Second, we enlarge SVM clustering by using a generalized ordered weighted averaging (OWA) operator to make multi-shpere SV clustering is capable of adaptively growing cell when a new point (but doesn’t belong to any existed cluster) is presented. Each sphere in the feature space corresponds to a cluster in original space and, whereby, it is possible to obtain the grade of fuzzy memberships, as well as cluster prototypes (sphere center) in partition. Third, we incorporate the concept of fuzzy set theory into the SVM regression. The parameters to be identified in SVM regression, such as the components within the weight vector and the bias term, are fuzzy numbers. The desired outputs in training samples are also fuzzy numbers. The SVM’s theory characterizes properties of learning machines which enable them to generalize well the unseen data and the fuzzy set theory might be very useful for finding a fuzzy structure in an evaluation system.
Yang, Hsin-Yu, and 楊新宇. "Transformation Model for Interval Censoring with a Cured Subgroup by Kernel-based Estimation." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/31888676456283846796.
Повний текст джерела淡江大學
統計學系碩士班
103
As time progresses, continuous development, there are more and more interval censoring data with clinical trials. Sometimes, it is hard to observe the exact time of event, but we know the observed failure time falls within a time period. In this thesis, we consider mixture cure models for interval censored data with a cured subgroup, where subjects in this subgroup are not susceptible to the event of interest. We suppose logistic regression to estimate cure proportion. In addition, we consider semiparametric transformation models to analysis the event data. We focus on reparametrizing the step function of unknown baseline hazard function by the logarithm of its jump sizes in Chapter 3, and a kernel-based approach for smooth estimation of unknown baseline hazard function in Chapter 4. The EM algorithm is developed for the estimation and simulation studies are conducted.
Su, Shao-Zu, and 蘇少祖. "Using Kernel Smoothing Approaches Imporves the Parameter Estimation based on Generalized Partial Credit Model." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/75909558041762230547.
Повний текст джерела國立臺中教育大學
教育測驗統計研究所
96
In this paper, a modified version of MMLE/EM is proposed. There are two modifications in the proposed algorithm. First, kernel density estimation technique is applied to estimate the distribution of ability parameter in E-step. Second, kernel density estimation technique is applied to estimate the item parameters and ability parameters with EAP in M-step. Finally, we use this methodology to estimate the ability and item parameters iteratively. This algorithm is named kernel smoothing - generalized partial credit model , KS-GPCM for short. In this paper, a simulation experiment based on the generalized partial credit model is conducted to compare the performances of PARSCALE and KS-GPCM. In the experiment, three types of distributions of ability parameters (normal, bi-mode and skewed distributions) are considered. Experimental results show as follow: (i) When distribution of ability parameter is normally distributed, RMSE of ability parameter of PARSCALE is less than KS-GPCM. (ii) When distributions of ability parameters are bimodal and skewness, RMSE of ability parameter of KS-GPCM is less than PARSCALE. (iii) When distribution of ability parameter is normally distributed, RMSE of slope and item step parameters of PARSCALE is less than KS-GPCM. (iv) When distributions of ability parameters are bimodal and skewness, RMSE of slope and item step parameters of KS-GPCM is less than PARSCALE.
Zhang, Rui. "Model selection techniques for kernel-based regression analysis using information complexity measure and genetic algorithms." 2007. http://etd.utk.edu/2007/ZhangRui.pdf.
Повний текст джерелаShun-Te, O., and 歐順德. "The Monte Carlo Simulation Study of The Hybrid Model of Generalized Hidden Markov Model and Kernel smoothing based Item Response Theory." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/02075678413122792655.
Повний текст джерела亞洲大學
資訊工程學系碩士班
94
Item characteristic curve is the central concept of the item response theory, the accuracy of ICC estimation of IRT model that is unable to prove better or not with mathematical or statistical method or the logic rule. The main purpose of this study rely on simulation to compare the accuracy of ICC estimation of four IRT Models, i.e. three-parameter logistic IRT model, the hybrid model of GHMM and 2PL-IRT, kernel smoothing based IRT, the hybrid model of GHMM and kernel smoothing based IRT. Simulation utilized MATLAB software to develop programs and simulate the data needed. Supposing discrimination parameter, difficulty parameter and ability parameter are normal distribution, guessing parameter is uniform distribution, item numbers are 25, there are six different numbers of examinees : 100,200,500,1000,1500 and 2000. According to this study, several findings have been concluded as follows: 1. The hybrid model of GHMM and kernel smoothing based IRT is better than the other’s IRT models for the accuracy of ICC estimation. 2. No matter parameter or nonparameter IRT model with GHMM is more accurate for the accuracy of ICC estimation. 3. The size of examinees will influence the accuracy of ICC estimation, more examinees are more accurate.
Liao, Wei-Chieh, and 廖偉捷. "Real-Time Surface Defect Inspection based on Single Kernel Multiple Threads Computing Model of Compute Unified Device Architecture." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/31581986489037776680.
Повний текст джерела中原大學
機械工程研究所
101
Techniques of surface defect detection play an important role in the product quality control. Human visual inspection is time-consuming, error-prone, and labor intensive. Due to the rapid development in the fields of machine vision, image processing, and high performance computing, a variety of different applications can benefit from the combination of technologies in these fields. A primary industry application is the automatic surface defect detection. In the thesis, for objects with a large surface area, the CUDA (Compute Unified Device Architecture) technology is adopted to satisfy both the high-speed and high-precision requirements on the surface defect detection. The CUDA is a heterogeneous computing platform and programming model that integrates the CPU (Central Processing Unit) and GPU (Graphics Processing Unit) components. In the proposed method, based on a GPU’s maximum allowable threads, the image of an object is divided into different image blocks. The image blocks are successively processed by the GPU, via concurrent threads, to determine the edges and number of defects in each single image block. On the other hand, the CPU determines the number of defects that spans adjacent image blocks, in order to obtain the correct number of defects in the entire image. Experimental results show that the algorithms developed in the thesis can accurately obtain the edges and number of defects inside an image, containing 2.4576×〖10〗^8 pixels, in less than one second.
Nguyen, Quang Anh. "Advanced methods and extensions for kernel-based object tracking." Phd thesis, 2010. http://hdl.handle.net/1885/150670.
Повний текст джерелаChun-Hsien, Lee. "Video Object Analysis Using Kernel-based Models and Spatiotemporal Similarity." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-2007200610102100.
Повний текст джерелаLee, Chun-Hsien, and 李俊賢. "Video Object Analysis Using Kernel-based Models and Spatiotemporal Similarity." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/27362489915766268020.
Повний текст джерела元智大學
電機工程學系
94
Video object segmentation plays an important role in many advanced application such as human-computer interaction, video surveillance, content-based video coding. In this paper we proposes a semantic video object segmentation system which combines spatiotemporal video segmentation and region tracking together to extract important semantic objects from videos. At beginning, the paper uses multiple cues to segment video frames to different regions. The cues include color, edges, motions, and kernel-based models. Since these features are complementary to each other, all desired regions can be well segmented from input frames even though they are captured from a non-stationary camera. Then, according to spatial information of each segmented region, we can construct a region adjacency graph (RAG) which can well record the relative relations between each region. Based on the RAG, we propose a Bayesian classifier which can group regions by properly checking their spatial and temporal similarities such that different regions will be merged and associated together to form a meaningful object. Since we include a kernel-based analysis into the designed classier, all desired semantic objects can be well extracted from video sequences. The kernel-based analysis can provide rich information for segmenting semantic objects if they are still in the background and cannot be identified using other features like motions. Experimental results have proved the superiority of the proposed method in object segmentation.
Konur, Savas, and Marian Gheorghe. "Proceedings of the Workshop on Membrane Computing, WMC 2016." 2016. http://hdl.handle.net/10454/8840.
Повний текст джерелаThis Workshop on Membrane Computing, at the Conference of Unconventional Computation and Natural Computation (UCNC), 12th July 2016, Manchester, UK, is the second event of this type after the Workshop at UCNC 2015 in Auckland, New Zealand*. Following the tradition of the 2015 Workshop the Proceedings are published as technical report. The Workshop consisted of one invited talk and six contributed presentations (three full papers and three extended abstracts) covering a broad spectrum of topics in Membrane Computing, from computational and complexity theory to formal verification, simulation and applications in robotics. All these papers – see below, but the last extended abstract, are included in this volume. The invited talk given by Rudolf Freund, “P SystemsWorking in Set Modes”, presented a general overview on basic topics in the theory of Membrane Computing as well as new developments and future research directions in this area. Radu Nicolescu in “Distributed and Parallel Dynamic Programming Algorithms Modelled on cP Systems” presented an interesting dynamic programming algorithm in a distributed and parallel setting based on P systems enriched with adequate data structure and programming concepts representation. Omar Belingheri, Antonio E. Porreca and Claudio Zandron showed in “P Systems with Hybrid Sets” that P systems with negative multiplicities of objects are less powerful than Turing machines. Artiom Alhazov, Rudolf Freund and Sergiu Ivanov presented in “Extended Spiking Neural P Systems with States” new results regading the newly introduced topic of spiking neural P systems where states are considered. “Selection Criteria for Statistical Model Checker”, by Mehmet E. Bakir and Mike Stannett, presented some early experiments in selecting adequate statistical model checkers for biological systems modelled with P systems. In “Towards Agent-Based Simulation of Kernel P Systems using FLAME and FLAME GPU”, Raluca Lefticaru, Luis F. Macías-Ramos, Ionuţ M. Niculescu, Laurenţiu Mierlă presented some of the advatages of implementing kernel P systems simulations in FLAME. Andrei G. Florea and Cătălin Buiu, in “An Efficient Implementation and Integration of a P Colony Simulator for Swarm Robotics Applications" presented an interesting and efficient implementation based on P colonies for swarms of Kilobot robots. *http://ucnc15.wordpress.fos.auckland.ac.nz/workshop-on-membrane-computingwmc- at-the-conference-on-unconventional-computation-natural-computation/
Feng-XuLi and 李豐旭. "Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Gait Recognition." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/q2e8w3.
Повний текст джерела國立成功大學
電機工程學系碩士在職專班
103
In this thesis, we propose a method to extract the human gait features from the surveillance video through Gabor wavelet transformation, and then we classify these features by kernel principle component analysis (PCA) with the fractional power polynomial model. Because human gait feature extraction can be categorized into spatial and temporal domain, we will discuss the gait features in these two domains. In order not to lose any information from the surveillance video, this thesis uses the spatial-temporal silhouette of the people walking in the surveillance video, then we can have the gait features by taking silhouette convolution with Gabor based wavelet transformation. We classify these features by kernel PCA with the fractional power polynomial model. Finally, we use Mahalanobis distance to measure the similarity between the gait features. The simulation and the experiment results show that Gabor-based kernel PCA with fractional power polynomial models for Gait recognition have a better performance.
Huang, Jhu-Yun, and 黃筑妘. "Liver Segmentation by Kernel-Based Deformable Shape Models in 3D medical Images." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/55967093346722649181.
Повний текст джерелаVicente, Sergio. "Apprentissage statistique avec le processus ponctuel déterminantal." Thesis, 2021. http://hdl.handle.net/1866/25249.
Повний текст джерелаThis thesis presents the determinantal point process, a probabilistic model that captures repulsion between points of a certain space. This repulsion is encompassed by a similarity matrix, the kernel matrix, which selects which points are more similar and then less likely to appear in the same subset. This point process gives more weight to subsets characterized by a larger diversity of its elements, which is not the case with the traditional uniform random sampling. Diversity has become a key concept in domains such as medicine, sociology, forensic sciences and behavioral sciences. The determinantal point process is considered a promising alternative to traditional sampling methods, since it takes into account the diversity of selected elements. It is already actively used in machine learning as a subset selection method. Its application in statistics is illustrated with three papers. The first paper presents the consensus clustering, which consists in running a clustering algorithm on the same data, a large number of times. To sample the initials points of the algorithm, we propose the determinantal point process as a sampling method instead of a uniform random sampling and show that the former option produces better clustering results. The second paper extends the methodology developed in the first paper to large-data. Such datasets impose a computational burden since sampling with the determinantal point process is based on the spectral decomposition of the large kernel matrix. We introduce two methods to deal with this issue. These methods also produce better clustering results than consensus clustering based on a uniform sampling of initial points. The third paper addresses the problem of variable selection for the linear model and the logistic regression, when the number of predictors is large. A Bayesian approach is adopted, using Markov Chain Monte Carlo methods with Metropolis-Hasting algorithm. We show that setting the determinantal point process as the prior distribution for the model space selects a better final model than the model selected by a uniform prior on the model space.
Tang, X., Qichun Zhang, X. Dai, and Y. Zou. "Neural membrane mutual coupling characterisation using entropy-based iterative learning identification." 2020. http://hdl.handle.net/10454/18180.
Повний текст джерелаThis paper investigates the interaction phenomena of the coupled axons while the mutual coupling factor is presented as a pairwise description. Based on the Hodgkin-Huxley model and the coupling factor matrix, the membrane potentials of the coupled myelinated/unmyelinated axons are quantified which implies that the neural coupling can be characterised by the presented coupling factor. Meanwhile the equivalent electric circuit is supplied to illustrate the physical meaning of this extended model. In order to estimate the coupling factor, a data-based iterative learning identification algorithm is presented where the Rényi entropy of the estimation error has been minimised. The convergence of the presented algorithm is analysed and the learning rate is designed. To verified the presented model and the algorithm, the numerical simulation results indicate the correctness and the effectiveness. Furthermore, the statistical description of the neural coupling, the approximation using ordinary differential equation, the measurement and the conduction of the nerve signals are discussed respectively as advanced topics. The novelties can be summarised as follows: 1) the Hodgkin-Huxley model has been extended considering the mutual interaction between the neural axon membranes, 2) the iterative learning approach has been developed for factor identification using entropy criterion, and 3) the theoretical framework has been established for this class of system identification problems with convergence analysis.
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 51807010, and in part by the Natural Science Foundation of Hunan under Grant 1541 and Grant 1734.
Research Development Fund Publication Prize Award winner, Nov 2020.