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ASEERVATHAM, SUJEEVAN. "A CONCEPT VECTOR SPACE MODEL FOR SEMANTIC KERNELS." International Journal on Artificial Intelligence Tools 18, no. 02 (April 2009): 239–72. http://dx.doi.org/10.1142/s0218213009000123.
Kernels are widely used in Natural Language Processing as similarity measures within inner-product based learning methods like the Support Vector Machine. The Vector Space Model (VSM) is extensively used for the spatial representation of the documents. However, it is purely a statistical representation. In this paper, we present a Concept Vector Space Model (CVSM) representation which uses linguistic prior knowledge to capture the meanings of the documents. We also propose a linear kernel and a latent kernel for this space. The linear kernel takes advantage of the linguistic concepts whereas the latent kernel combines statistical and linguistic concepts. Indeed, the latter kernel uses latent concepts extracted by the Latent Semantic Analysis (LSA) in the CVSM. The kernels were evaluated on a text categorization task in the biomedical domain. The Ohsumed corpus, well known for being difficult to categorize, was used. The results have shown that the CVSM improves performance compared to the VSM.
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DIOŞAN, LAURA, ALEXANDRINA ROGOZAN, and JEAN-PIERRE PECUCHET. "LEARNING SVM WITH COMPLEX MULTIPLE KERNELS EVOLVED BY GENETIC PROGRAMMING." International Journal on Artificial Intelligence Tools 19, no. 05 (October 2010): 647–77. http://dx.doi.org/10.1142/s0218213010000352.
Classic kernel-based classifiers use only a single kernel, but the real-world applications have emphasized the need to consider a combination of kernels — also known as a multiple kernel (MK) — in order to boost the classification accuracy by adapting better to the characteristics of the data. Our purpose is to automatically design a complex multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. In our model, each GP chromosome is a tree that encodes the mathematical expression of a multiple kernel. The evolutionary search process of the optimal MK is guided by the fitness function (or efficiency) of each possible MK. The complex multiple kernels which are evolved in this manner (eCMKs) are compared to several classic simple kernels (SKs), to a convex linear multiple kernel (cLMK) and to an evolutionary linear multiple kernel (eLMK) on several real-world data sets from UCI repository. The numerical experiments show that the SVM involving the evolutionary complex multiple kernels perform better than the classic simple kernels. Moreover, on the considered data sets, the new multiple kernels outperform both the cLMK and eLMK — linear multiple kernels. These results emphasize the fact that the SVM algorithm requires a combination of kernels more complex than a linear one in order to boost its performance.
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Segera, Davies, Mwangi Mbuthia, and Abraham Nyete. "Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis." BioMed Research International 2019 (December 16, 2019): 1–11. http://dx.doi.org/10.1155/2019/4085725.
Determining an optimal decision model is an important but difficult combinatorial task in imbalanced microarray-based cancer classification. Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel, and its parameters. To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis (PCA), and multiclass support vector machine (MCSVM). The MCSVM is based on a hybrid kernel, i.e., linear-Gaussian-polynomial (LGP) that combines the advantages of three standard kernels (linear, Gaussian, and polynomial) in a novel manner, where the linear kernel is linearly combined with the Gaussian kernel embedding the polynomial kernel. Further, this paper proves and makes sure that the LGP kernel confirms the features of a valid kernel. In order to reveal the effectiveness of our model, several experiments were conducted and the obtained results compared between our model and other three single kernel-based models, namely, PSO-PCA-L-MCSVM (utilizing a linear kernel), PSO-PCA-G-MCSVM (utilizing a Gaussian kernel), and PSO-PCA-P-MCSVM (utilizing a polynomial kernel). In comparison, two dual and two multiclass imbalanced standard microarray datasets were used. Experimental results in terms of three extended assessment metrics (F-score, G-mean, and Accuracy) reveal the superior global feature extraction, prediction, and learning abilities of this model against three single kernel-based models.
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Nehra, Rahul, and Kamalpreet Kaur. "AI-based Optimization of Tensile Strength of the Cement Concrete Incorporating Recycled Mixed Plastic Fine used in Road Construction." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (November 30, 2023): 198–203. http://dx.doi.org/10.22214/ijraset.2023.56481.
Abstract: One of the main problems in materials science and engineering is predicting the tensile strength of materials. In this study, we investigate how to model and forecast tensile strength (Tensile Strength in Mpa) based on different material attributes using Support Vector Regression (SVR) using Linear and Polynomial Kernels. The dataset includes the following details: plastic type, fine aggregate ratio, water/cement ratio, cement content, and associated tensile strength values. This work has two main goals: (1) to assess the predictive power of SVR models with various kernel functions and (2) to examine the significance of unique material attributes for prediction. To simulate the link between the input features and tensile strength, we used SVR in conjunction with a Linear Kernel. The final model included insightful information on how each feature affected the forecast. Our results show that the Polynomial Kernel SVR model may better reflect the complex interactions among the material attributes than the Linear Kernel SVR model, despite being more interpretable. Better prediction performance was offered by the Polynomial Kernel SVR, which also revealed the non-linear dependencies in the data.
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Andrade-Girón, Daniel, Edgardo Carreño-Cisneros, Cecilia Mejía-Dominguez, Julia Velásquez-Gamarra, William Marín-Rodriguez, Henry Villarreal-Torres, and Rosana Meleán-Romero. "Support vector machine with optimized parameters for the classification of patients with COVID-19." EAI Endorsed Transactions on Pervasive Health and Technology 9 (June 20, 2023): e8. http://dx.doi.org/10.4108/eetpht.9.3472.
Introduction. The COVID-19 pandemic has had a significant impact worldwide, especially in health, where it is crucial to identify patients at high risk of clinical deterioration early.
Objective. This study aimed to design a model based on the support vector machine (SVM) algorithm, optimizing its parameters to classify patients with suspected COVID-19.
Methodology. One thousand patient records from two health establishments in Peru were used. After applying data preprocessing and variable engineering, the sample was reduced to 700 records. The construction of the model followed a machine learning methodology, using the linear, polynomial, sigmoid, and radial kernel functions, along with their estimated optimal parameters, to ensure the best performance.
Results. The results revealed that the SVM model with the linear and sigmoid kernels presented an accuracy of 95%, surpassing the polynomial kernel with 94% and the radial kernel (RBF) with 94%. In addition, a value of 0.92 was obtained for Cohen's kappa, which measures the degree of agreement between the predictions of the machine learning model and the actual results, which indicates an excellent deal for the linear and sigmoid kernel.
Conclusions. In conclusion, the SVM model with linear and sigmoid kernels could be a valuable tool for identifying patients at high risk of clinical deterioration in the context of the COVID-19 pandemic.
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Caraka, Rezzy Eko, Hasbi Yasin, and Adi Waridi Basyiruddin. "Peramalan Crude Palm Oil (CPO) Menggunakan Support Vector Regression Kernel Radial Basis." Jurnal Matematika 7, no. 1 (June 10, 2017): 43. http://dx.doi.org/10.24843/jmat.2017.v07.i01.p81.
Recently, instead of selecting a kernel has been proposed which uses SVR, where the weight of each kernel is optimized during training. Along this line of research, many pioneering kernel learning algorithms have been proposed. The use of kernels provides a powerful and principled approach to modeling nonlinear patterns through linear patterns in a feature space. Another bene?t is that the design of kernels and linear methods can be decoupled, which greatly facilitates the modularity of machine learning methods. We perform experiments on real data sets crude palm oil prices for application and better illustration using kernel radial basis. We see that evaluation gives a good to fit prediction and actual also good values showing the validity and accuracy of the realized model based on MAPE and R2.
Keywords: Crude Palm Oil; Forecasting; SVR; Radial Basis; Kernel
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IVAN, KOMANG CANDRA, I. WAYAN SUMARJAYA, and MADE SUSILAWATI. "ANALISIS MODEL REGRESI NONPARAMETRIK SIRKULAR-LINEAR BERGANDA." E-Jurnal Matematika 5, no. 2 (May 31, 2016): 52. http://dx.doi.org/10.24843/mtk.2016.v05.i02.p121.
Circular data are data which the value in form of vector is circular data. Statistic analysis that is used in analyzing circular data is circular statistics analysis. In regression analysis, if any of predictor or response variables or both are circular then the regression analysis used is called circular regression analysis. Observation data in circular statistic which use direction and time units usually don’t satisfy all of the parametric assumptions, thus making nonparametric regression as a good solution. Nonparametric regression function estimation is using epanechnikov kernel estimator for the linier variables and von Mises kernel estimator for the circular variable. This study showed that the result of circular analysis by using circular descriptive statistic is better than common statistic. Multiple circular-linier nonparametric regressions with Epanechnikov and von Mises kernel estimator didn’t create estimation model explicitly as parametric regression does, but create estimation from its observation knots instead.
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Sunitha, Lingam, and M. Bal Raju. "Multi-class classification for large datasets with optimized SVM by non-linear kernel function." Journal of Physics: Conference Series 2089, no. 1 (November 1, 2021): 012015. http://dx.doi.org/10.1088/1742-6596/2089/1/012015.
Abstract Most important part of Support Vector Machines(SVM) are the kernels. Although there are several widely used kernel functions, a carefully designed kernel will help improve the accuracy of SVM. The proposed work aims to develop a new kernel function for a multi-class support vector machine, perform experiments on various data sets, and compare them with other classification methods. Directly it is not possible multiclass classification with SVM. In this proposed work first designed a model for binary class then extended with the one-verses-all approach. Experimental results have proved the efficiency of the new kernel function. The proposed kernel reduces misclassification and time. Other classification methods observed better results for some data sets collected from the UCI repository.
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Jan, A. R. "An Asymptotic Model for Solving Mixed Integral Equation in Position and Time." Journal of Mathematics 2022 (August 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/8063971.
In this paper, we considered a mixed integral equation (MIE) of the second kind in the space L 2 − b , b × C 0 , T , T < 1. The kernel of position has a singularity and takes some different famous forms, while the kernels of time are positive and continuous. Using an asymptotic method of separating the variables, we have a Fredholm integral equation (FIE) in position with variable parameters in time. Then, using the Toeplitz matrix method (TMM), we obtain a linear algebraic system (LAS) that can be solved numerically. Some applications with the aid of the maple 18 program are discussed when the kernel takes Coleman function, Cauchy kernel, Hilbert kernel, and a generalized logarithmic function. Also the error estimate, in each case, is computed.
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Lumbanraja, Favorisen Rossyking, Reza Aji Saputra, Kurnia Muludi, Astria Hijriani, and Akmal Junaidi. "IMPLEMENTASI SUPPORT VECTOR MACHINE DALAM MEMPREDIKSI HARGA RUMAH PADA PERUMAHAN DI KOTA BANDAR LAMPUNG." Jurnal Pepadun 2, no. 3 (December 1, 2021): 327–35. http://dx.doi.org/10.23960/pepadun.v2i3.90.
Machine Learning has been widely used in terms of predictions for analyzing datasets. One method of Machine Learning is Support Vector Machine (SVM). The house has an important role in the survival of human beings. With the times, many developers are competing to build housing. The purpose of this study is to predicted the housing cost using Support Vector Machine. The data in this research used the data of house in Bandar lampung, the price, the location and the building specification. The amount of data used 51 datas and 33 variables with regression and classification, also used 3 kernels and it's model, 12 times first trial and next 6 experiments done with fitur selection. The trial result was kernel regression polynomial model reached the highest R 2 that was 95,99% linear kernel and gaussian kernel reached R 2 90,99% and 81,43% each. While in accuration classification model trial is obtained in 8 class of gaussian kernel as big as 91,18%, and linear kernel and polynimonal kernel get an accuracy of 90,20% and 89,90%.
Roberts, Gareth James. "Monitoring land cover dynamics using linear kernel-driven BRDF model parameter temporal trajectories." Thesis, University College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407145.
Hu, Zonghui. "Semiparametric functional data analysis for longitudinal/clustered data: theory and application." Texas A&M University, 2004. http://hdl.handle.net/1969.1/3088.
Semiparametric models play important roles in the field of biological statistics. In this dissertation, two types of semiparametic models are to be studied. One is the partially linear model, where the parametric part is a linear function. We are to investigate the two common estimation methods for the partially linear models when the data is correlated  longitudinal or clustered. The other is a semiparametric model where a latent covariate is incorporated in a mixed effects model. We will propose a semiparametric approach for estimation of this model and apply it to the study on colon carcinogenesis.
First, we study the profilekernel and backfitting methods in partially linear models for clustered/longitudinal data. For independent data, despite the potential rootn inconsistency of the backfitting estimator noted by Rice (1986), the two estimators have the same asymptotic variance matrix as shown by Opsomer and Ruppert (1999). In this work, theoretical comparisons of the two estimators for multivariate responses are investigated. We show that, for correlated data, backfitting often produces a larger asymptotic variance than the profilekernel method; that is, in addition to its bias problem, the backfitting estimator does not have the same asymptotic efficiency as the profilekernel estimator when data is correlated. Consequently, the common practice of using the backfitting method to compute profilekernel estimates is no longer advised. We illustrate this in detail by following Zeger and Diggle (1994), Lin and Carroll (2001) with a working independence covariance structure for nonparametric estimation and a correlated covariance structure for parametric estimation. Numerical performance of the two estimators is investigated through a simulation study. Their application to an ophthalmology dataset is also described.
Next, we study a mixed effects model where the main response and covariate variables are linked through the positions where they are measured. But for technical reasons, they are not measured at the same positions. We propose a semiparametric approach for this misaligned measurements problem and derive the asymptotic properties of the semiparametric estimators under reasonable conditions. An application of the semiparametric method to a colon carcinogenesis study is provided. We find that, as compared with the corn oil supplemented diet, fish oil supplemented diet tends to inhibit the increment of bcl2 (oncogene) gene expression in rats when the amount of DNA damage increases, and thus promotes apoptosis.
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Kayhan, Belgin. "Parameter Estimation In Generalized Partial Linear Modelswith Tikhanov Regularization." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612530/index.pdf.
Regression analysis refers to techniques for modeling and analyzing several variables in statistical learning. There are various types of regression models. In our study, we analyzed Generalized Partial Linear Models (GPLMs), which decomposes input variables into two sets, and additively combines classical linear models with nonlinear model part. By separating linear models from nonlinear ones, an inverse problem method Tikhonov regularization was applied for the nonlinear submodels separately, within the entire GPLM. Such a particular representation of submodels provides both
a better accuracy and a better stability (regularity) under noise in the data.
We aim to smooth the nonparametric part of GPLM by using a modified form of Multiple Adaptive Regression Spline (MARS) which is very useful for high-dimensional problems and does not impose any specific relationship between the predictor and
dependent variables. Instead, it can estimate the contribution of the basis functions so that both the additive and interaction effects of the predictors are allowed to determine
the dependent variable. The MARS algorithm has two steps: the forward and backward stepwise algorithms. In the rst one, the model is built by adding basis functions until a maximum level of complexity is reached. On the other hand, the backward stepwise algorithm starts with removing the least significant basis functions from the model.
In this study, we propose to use a penalized residual sum of squares (PRSS) instead of the backward stepwise algorithm and construct PRSS for MARS as a Tikhonov regularization problem. Besides, we provide numeric example with two data sets one has interaction and the other one does not have. As well as studying the regularization of the nonparametric part, we also mention theoretically the regularization
of the parametric part. Furthermore, we make a comparison between Infinite Kernel Learning (IKL) and Tikhonov regularization by using two data sets, with the difference
consisting in the (non-)homogeneity of the data set. The thesis concludes with an outlook on future research.
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Ozier-Lafontaine, Anthony. "Kernel-based testing and their application to single-cell data." Electronic Thesis or Diss., Ecole centrale de Nantes, 2023. http://www.theses.fr/2023ECDN0025.
Les technologies de sequençage en cellule unique mesurent des informations à l’échelle de chaque cellule d’une population. Les données issues de ces technologies présentent de nombreux défis : beaucoup d’observations en grande dimension et souvent parcimonieuses. De nombreuses expériences de biologie consistent à comparer des conditions.L’objet de la thèse est de développer un ensemble d’outils qui compare des échantillons de données issues des technologies de séquençage en cellule unique afin de détecter et décrire les différences qui existent. Pour cela, nous proposons d’appliquer les tests de comparaison de deux échantillons basés sur les méthodes à noyaux existants. Nous proposons de généraliser ces tests à noyaux pour les designs expérimentaux quelconques, ce test s’inspire du test de la trace de Hotelling- Lawley. Nous implémentons pour la première fois ces tests à noyaux dans un packageR et Python nommé ktest, et nos applications sur données simulées et issues d’expériences démontrent leurs performances. L’application de ces méthodes à des données expérimentales permet d’identifier les observations qui expliquent les différences détectées. Enfin, nous proposons une implémentation efficace de ces tests basée sur des factorisations matricielles de type Nyström, ainsi qu’un ensemble d’outils de diagnostic et d’interprétation des résultats pour rendre ces méthodes accessibles et compréhensibles par des nonspécialistes Single-cell technologies generate data at the single-cell level. They are coumposed of hundreds to thousands of observations (i.e. cells) and tens of thousands of variables (i.e. genes). New methodological challenges arose to fully exploit the potentialities of these complex data. A major statistical challenge is to distinguish biological informationfrom technical noise in order to compare conditions or tissues. This thesis explores the application of kernel testing on single-cell datasets in order to detect and describe the potential differences between compared conditions.To overcome the limitations of existing kernel two-sample tests, we propose a kernel test inspired from the Hotelling-Lawley test that can apply to any experimental design. We implemented these tests in a R and Python package called ktest that is their first useroriented implementation. We demonstrate the performances of kernel testing on simulateddatasets and on various experimental singlecell datasets. The geometrical interpretations of these methods allows to identify the observations leading a detected difference. Finally, we propose a Nyström-based efficient implementationof these kernel tests as well as a range of diagnostic and interpretation tools
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Vassura, Edoardo. "Path integrals on curved space and the worldline formalism." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13448/.
Lo scopo primario di questa tesi e' l’analisi di una nuova procedura di regolarizzazione di path integral su spazi curvi, presentata inizialmente dal fisico J. Guven e applicata al caso di una teoria di campo scalare , ma mai utilizzata per svolgere ulteriori calcoli espliciti. Questa procedura, se corretta, permetterebbe di utilizzare il formalismo di path integral su spazi piatti anche nel caso in cui la varieta' di background risulti localmente curva. Tale procedura trasforma di fatto un modello sigma non lineare in un modello efficace lineare, permettando pertanto di aggirare le usuali complicazioni dovute alla generalizzazione di path integral. Una prova diretta della correttezza della procedura di Guven sembra mancare in letteratura: per questo motivo in questa tesi verranno eseguiti vari test volti a tale verifica. Alcuni errori sono stati riscontrati nella proposta iniziale, tra i quali un termine di potenziale che risulta essere non corretto. Ad ogni modo siamo stati in grado di identificare un potenziale che permetta di riprodurre correttamente i primi due coefficienti dell’espansione in serie dell’heat kernel. Utilizzando lo stesso metodo abbiamo poi cercato di ottenere il successivo coefficiente dell’espansione (cubico in termini di curvatura): il risultato ottenuto non risulta essere corretto, cosa che segnala il fallimento di tale metodo ad ordini superiori. Visti tali risultati preliminari, siamo stati indotti a considerare una classe speciale di spazi curvi, quella degli spazi massimamente simmetrici, trovando invece che su tali spazi la procedura di Guven riproduce i risultati corretti. Come verifica abbiamo ottenuto la parte diagonale dell’heat kernel, che ́ stata poi utilizzata per riprodurre l’anomalia di traccia di tipo A per campi scalari in dimensioni arbitrarie fino a D = 12. Questi risultati sono in accordo con quelli attesi. Viene pertanto fornita una prova della validita' di tale procedura su questi spazi.
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Song, Song. "Confidence bands in quantile regression and generalized dynamic semiparametric factor models." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2010. http://dx.doi.org/10.18452/16341.
In vielen Anwendungen ist es notwendig, die stochastische Schwankungen der maximalen Abweichungen der nichtparametrischen Schätzer von Quantil zu wissen, zB um die verschiedene parametrische Modelle zu überprüfen. Einheitliche Konfidenzbänder sind daher für nichtparametrische Quantil Schätzungen der Regressionsfunktionen gebaut. Die erste Methode basiert auf der starken Approximation der empirischen Verfahren und Extremwert-Theorie. Die starke gleichmäßige Konsistenz liegt auch unter allgemeinen Bedingungen etabliert. Die zweite Methode beruht auf der Bootstrap Resampling-Verfahren. Es ist bewiesen, dass die Bootstrap-Approximation eine wesentliche Verbesserung ergibt. Der Fall von mehrdimensionalen und diskrete Regressorvariablen wird mit Hilfe einer partiellen linearen Modell behandelt. Das Verfahren wird mithilfe der Arbeitsmarktanalysebeispiel erklärt. Hoch-dimensionale Zeitreihen, die nichtstationäre und eventuell periodische Verhalten zeigen, sind häufig in vielen Bereichen der Wissenschaft, zB Makroökonomie, Meteorologie, Medizin und Financial Engineering, getroffen. Der typische Modelierungsansatz ist die Modellierung von hochdimensionalen Zeitreihen in Zeit Ausbreitung der niedrig dimensionalen Zeitreihen und hoch-dimensionale zeitinvarianten Funktionen über dynamische Faktorenanalyse zu teilen. Wir schlagen ein zweistufiges Schätzverfahren. Im ersten Schritt entfernen wir den Langzeittrend der Zeitreihen durch Einbeziehung Zeitbasis von der Gruppe Lasso-Technik und wählen den Raumbasis mithilfe der funktionalen Hauptkomponentenanalyse aus. Wir zeigen die Eigenschaften dieser Schätzer unter den abhängigen Szenario. Im zweiten Schritt erhalten wir den trendbereinigten niedrig-dimensionalen stochastischen Prozess (stationär). In many applications it is necessary to know the stochastic fluctuation of the maximal deviations of the nonparametric quantile estimates, e.g. for various parametric models check. Uniform confidence bands are therefore constructed for nonparametric quantile estimates of regression functions. The first method is based on the strong approximations of the empirical process and extreme value theory. The strong uniform consistency rate is also established under general conditions. The second method is based on the bootstrap resampling method. It is proved that the bootstrap approximation provides a substantial improvement. The case of multidimensional and discrete regressor variables is dealt with using a partial linear model. A labor market analysis is provided to illustrate the method. High dimensional time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science, e.g. macroeconomics, meteorology, medicine and financial engineering. One of the common approach is to separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via dynamic factor analysis. We propose a two-step estimation procedure. At the first step, we detrend the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under the dependent scenario. At the second step, we obtain the detrended low dimensional stochastic process (stationary).
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Piccini, Jacopo. "Data Dependent Convergence Guarantees for Regression Problems in Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24218/.
It has been recently demonstrated that the artificial neural networks’ (ANN) learning under gradient descent method, can be studied using neural tangent kernel (NTK). This thesis’ goal is to show how techniques related to control theory, can be applied to model and improve the hyperparameters training dynamics. Moreover, it will be proven how by using methods from linear parameter varying (LPV) theory can allow the exact representation of the learning dynamics over its whole domain.
The first part of the thesis is dedicated to the modelling and analysis of the system. The modelling of simple ANNs is hereby suggested and a method to expand this approach to larger networks is proposed. After the first part, the LPV system model’s different properties are analysed using different methods. After the modelling and analysis phase, the focus will be shifted on how to improve the neural network both in terms of stability and performances. This improvement is achieved by using state feedback on the LPV system. After setting up the control architecture, controllers based on different methods, such as optimal control and robust control, are then synthesized and their performances are compared.
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Vlachos, 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.
A powerful technique is presented for joint blind channel estimation and carrier offset method for code- division multiple access (CDMA) communication systems. The new technique combines singular value decomposition (SVD) analysis with carrier offset parameter. Current blind methods sustain a high computational complexity as they require the computation of a large SVD twice, and they are sensitive to accurate knowledge of the noise subspace rank. The proposed method overcomes both problems by computing the SVD only once. Extensive simulations using MatLab demonstrate the robustness of the proposed scheme and its performance is comparable to other existing SVD techniques with significant lower computational as much as 70% cost because it does not require knowledge of the rank of the noise sub-space. Also a kernel based equalization for CDMA communication systems is proposed, designed and simulated using MatLab. The proposed method in CDMA systems overcomes all other methods.
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Fan, Liangdong. "ESTIMATION IN PARTIALLY LINEAR MODELS WITH CORRELATED OBSERVATIONS AND CHANGE-POINT MODELS." UKnowledge, 2018. https://uknowledge.uky.edu/statistics_etds/32.
Methods of estimating parametric and nonparametric components, as well as properties of the corresponding estimators, have been examined in partially linear models by Wahba [1987], Green et al. [1985], Engle et al. [1986], Speckman [1988], Hu et al. [2004], Charnigo et al. [2015] among others. These models are appealing due to their flexibility and wide range of practical applications including the electricity usage study by Engle et al. [1986], gum disease study by Speckman [1988], etc., wherea parametric component explains linear trends and a nonparametric part captures nonlinear relationships.
The compound estimator (Charnigo et al. [2015]) has been used to estimate the nonparametric component of such a model with multiple covariates, in conjunction with linear mixed modeling for the parametric component. These authors showed, under a strict orthogonality condition, that parametric and nonparametric component estimators could achieve what appear to be (nearly) optimal rates, even in the presence of subject-specific random effects.
We continue with research on partially linear models with subject-specific random intercepts. Inspired by Speckman [1988], we propose estimators of both parametric and nonparametric components of a partially linear model, where consistency is achievable under an orthogonality condition. We also examine a scenario without orthogonality to find that bias could still exist asymptotically. The random intercepts accommodate analysis of individuals on whom repeated measures are taken. We illustrate our estimators in a biomedical case study and assess their finite-sample performance in simulation studies.
Jump points have often been found within the domain of nonparametric models (Muller [1992], Loader [1996] and Gijbels et al. [1999]), which may lead to a poor fit when falsely assuming the underlying mean response is continuous. We study a specific type of change-point where the underlying mean response is continuous on both left and right sides of the change-point. We identify the convergence rate of the estimator proposed in Liu [2017] and illustrate the result in simulation studies.
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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.
Microbiome plays an important role in human health. The analysis of association between microbiome and clinical outcome has become an active direction in biostatistics research. Testing the microbiome effect on clinical phenotypes directly using operational taxonomic unit abundance data is a challenging problem due to the high dimensionality, non-normality and phylogenetic structure of the data. Most of the studies only focus on describing the change of microbe population that occur in patients who have the specific clinical condition. Instead, a statistical strategy utilizing distance-based or similarity-based non-parametric testing, in which a distance or similarity measure is defined between any two microbiome samples, is developed to assess association between microbiome composition and outcomes of interest. Despite the improvements, this test is still not easily interpretable and not able to adjust for potential covariates. A novel approach, kernel-based semi-parametric regression framework, is applied in evaluating the association while controlling the covariates. The framework utilizes a kernel function which is a measure of similarity between samples' microbiome compositions and characterizes the relationship between the microbiome and the outcome of interest. This kernel-based regression model, however, cannot be applied in longitudinal studies since it could not model the correlation between the repeated measurements. We proposed microbiome association exact tests (MAETs) in linear mixed model can deal with longitudinal microbiome data. MAETs can test not only the effect of overall microbiome but also the effect from specific cluster of the OTUs while controlling for others by introducing more random effects in the model. The current methods for multiple variance component testing are based on either asymptotic distribution or parametric bootstrap which require large sample size or high computational cost. The exact (R)LRT tests, an computational efficient and powerful testing methodology, was derived by Crainiceanu. Since the exact (R)LRT can only be used in testing one variance component, we proposed an approach that combines the recent development of exact (R)LRT and a strategy for simplifying linear mixed model with multiple variance components to a single case. The Monte Carlo simulation studies present correctly controlled type I error and provided superior power in testing association between microbiome and outcomes in longitudinal studies. Finally, the MAETs were applied to longitudinal pulmonary microbiome datasets to demonstrate that microbiome composition is associated with lung function and immunological outcomes. We also successfully found two interesting genera Prevotella and Veillonella which are associated with forced vital capacity.
This chapter explores several of the most common and useful approaches to atmospheric data fitting as well as the process of using air mass factors to produce vertical atmospheric column abundances from line-of-sight slant columns determined by data fitting. An atmospheric spectrum or other type of atmospheric sounding is usually fitted to a parameterized physical model by minimizing a cost function, usually chi-squared. Linear fitting, when the model of the measurements is linear in the model parameters is described, followed by the more common nonlinear fitting case. For nonlinear fitting, the standard Levenberg-Marquardt method is described, followed by the use of optimal estimation, one of several retrieval methods that make use of a priori information to providing regularization for the solution. In the context of optimal estimation, weighting functions, contribution functions, and averaging kernels are described. The Twomey-Tikhonov regularization procedure is presented. Correlated parameters, with the important example of Earth’s atmospheric ozone, are discussed.
Частини книг з теми "Kernel linear model":
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Lee, John, Jow-Ran Chang, Lie-Jane Kao, and Cheng-Few Lee. "Kernel Linear Model." In Essentials of Excel VBA, Python, and R, 261–77. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-14283-3_12.
Zhang, Yuehua, Peng Zhang, and Yong Shi. "Kernel Based Regularized Multiple Criteria Linear Programming Model." In Lecture Notes in Computer Science, 625–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01973-9_70.
Fateh, Rachid, Anouar Darif, and Said Safi. "Identification of the Linear Dynamic Parts of Wiener Model Using Kernel and Linear Adaptive." In Advanced Intelligent Systems for Sustainable Development (AI2SD’2020), 387–400. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90639-9_31.
Wong, Leon, Zhu-Hong You, Yu-An Huang, Xi Zhou, and Mei-Yuan Cao. "A Gaussian Kernel Similarity-Based Linear Optimization Model for Predicting miRNA-lncRNA Interactions." In Intelligent Computing Theories and Application, 316–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60802-6_28.
Yamanishi, Yoshihiro. "Linear and Kernel Model Construction Methods for Predicting Drug–Target Interactions in a Chemogenomic Framework." In Methods in Molecular Biology, 355–68. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8639-2_12.
Serjam, Chanakya, and Akito Sakurai. "Analyzing Performance of High Frequency Currency Rates Prediction Model Using Linear Kernel SVR on Historical Data." In Intelligent Information and Database Systems, 498–507. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54472-4_47.
Dhankhar, Amita, and Kamna Solanki. "Predicting Student’s Performance Using Linear Kernel Principal Component Analysis and Recurrent Neural Network (LKPCA-RNN) Model." In Proceedings of Data Analytics and Management, 637–46. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6285-0_51.
Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Regularization in Reproducing Kernel Hilbert Spaces for Linear System Identification." In Regularized System Identification, 247–311. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_7.
AbstractIn the previous parts of the book, we have studied how to handle linear system identification by using regularized least squares (ReLS) with finite-dimensional structures given, e.g., by finite impulse response (FIR) models. In this chapter, we cast this approach in the RKHS framework developed in the previous chapter. We show that ReLS with quadratic penalties can be reformulated as a function estimation problem in the finite-dimensional RKHS induced by the regularization matrix. This leads to a new paradigm for linear system identification that provides also new insights and regularization tools to handle infinite-dimensional problems, involving, e.g., IIR and continuous-time models. For all this class of problems, we will see that the representer theorem ensures that the regularized impulse response is a linear and finite combination of basis functions given by the convolution between the system input and the kernel sections. We then consider the issue of kernel estimation and introduce several tuning methods that have close connections with those related to the regularization matrix discussed in Chap. 10.1007/978-3-030-95860-2_3. Finally, we introduce the notion of stable kernels, that induce RKHSs containing only absolutely summable impulse responses and study minimax properties of regularized impulse response estimation.
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Lotufo, Rafael, Steven She, Thorsten Berger, Krzysztof Czarnecki, and Andrzej Wąsowski. "Evolution of the Linux Kernel Variability Model." In Software Product Lines: Going Beyond, 136–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15579-6_10.
Kirchner, Rosane M., Reinaldo C. Souza, and Flávio A. Ziegelmann. "Identification of the Structure of Linear and Non-Linear Time Series Models, Using Nonparametric Local Linear Kernel Estimation." In Soft Methodology and Random Information Systems, 589–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44465-7_73.
Тези доповідей конференцій з теми "Kernel linear model":
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De Luca, Patrick Medeiros, and Wemerson Delcio Parreira. "Simulação do comportamento estocástico do algoritmo KLMS com diferentes kernels." In Computer on the Beach. Itajaí: Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p004-006.
The kernel least-mean-square (KLMS) algorithm is a popular algorithmin nonlinear adaptive filtering due to its simplicity androbustness. In kernel adaptive filtering, the statistics of the inputto the linear filter depends on the kernel and its parameters. Moreover,practical implementations on systems estimation require afinite non-linearity model order. In order to obtain finite ordermodels, many kernelized adaptive filters use a dictionary of kernelfunctions. Dictionary size also depends on the kernel and itsparameters. Therefore, KLMS may have different performanceson the estimation of a nonlinear system, the time of convergence,and the accuracy using a different kernel. In order to analyze theperformance of KLMS with different kernels, this paper proposesthe use of the Monte Carlo simulation of both steady-state and thetransient behavior of the KLMS algorithm using different types ofkernel functions and Gaussian inputs.
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Fang, Yudong, Zhenfei Zhan, Junqi Yang, Jun Lu, and Chong Chen. "A Mixed-Kernel-Based Support Vector Regression Model for Automotive Body Design Optimization." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67669.
Finite Element (FE) models are commonly used for automotive body design. However, even with increasing speed of computers, the FE-based simulation models are still too time-consuming when the models are complex. To improve the computational efficiency, SVR, a potential approximate model, has been widely used as the surrogate of FE model for crashworthiness optimization design. Generally, in the traditional SVR, when dealing with nonlinear data, the single kernel function based projection can’t fully cover data distribution characteristics. In order to eliminate the limitations of single kernel SVR, a mixed-kernel-based SVR (MKSVR) is proposed in this research. The mixed kernel is constructed based on the linear combination of radial basis kernel function and polynomial kernel function. Through the particle swarm optimization algorithm, the parameters of the mixed kernel SVR are optimized. Then the proposed MKSVR is applied to automotive body design optimization. The application of MKSVR is demonstrated by a vehicle design problem for weight reduction while satisfying safety constraints on X direction acceleration and Crush Distance. A comparison study for SVR and MKSVR in application indicates MKSVR surpasses SVR in model accuracy.
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Pillonetto, Gianluigi, Tianshi Chen, and Lennart Ljung. "Kernel-based model order selection for identification and prediction of linear dynamic systems." In 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760702.
AlSaihati, Ahmed, Salaheldin Elkatatny, Hani Gamal, and Abdulazeez Abdulraheem. "A Statistical Machine Learning Model to Predict Equivalent Circulation Density ECD while Drilling, Based on Principal Components Analysis PCA." In SPE/IADC Middle East Drilling Technology Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/202101-ms.
Abstract Mathematical equations, based on conservation of mass and momentum, are used to determine the ECD at different depths in the wellbore. However, such equations do not consider important factors that have a influence on the ECD such as: (i) bottom hole temperature, (ii) pipe rotation and eccentricity, and (iii) wellbore roughness. Thus, discrepancy between the calculated ECDs and actual ones has been reported in the literature. This paper aims to explore how artificial intelligence (AI) and machine learning (ML) could provide real-time accurate prediction of the ECD, to have more insight and management of wellbore downhole conditions. For this purpose, a supervised ML algorithm, support vector machine (SVM), based on principal components analysis (PCA), was developed. Actual field data of Well-1 including drilling surface parameters and ECDs, measured by downhole sensors, were collected to develop a classical SVM model. The dataset was split with an 80/20 training-testing data ratio. Sensitivity analysis with different SVM parameters such as regularization parameter C, gamma, kernel type (linear, radial basis function "RBF") was performed. The performance of the model was assessed in terms of root mean square error (RMSE) and coefficient of determination (R2). Afterward, PCA was applied to the dataset of Well-1 to develop an SVM model using the transformed dataset in PCA space. The performance of the model while using different numbers of principal components was evaluated. The results showed that the classical SVM with the linear kernel predicted the ECD with RMSE of 0.53 and R2 of 0.97 in the training set, while RMSE and R2 were 0.56 and 0.97 respectively in the testing set. The PCA-based SVM model, with the linear kernel and four principal components (93.53% variation of the dataset), predicted the ECD with RMSE 0.79 and R2 of 0.95 in the testing set.
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Wang, Yi, Nan Xue, Xin Fan, Jiebo Luo, Risheng Liu, Bin Chen, Haojie Li, and Zhongxuan Luo. "Fast Factorization-free Kernel Learning for Unlabeled Chunk Data Streams." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/393.
Data stream analysis aims at extracting discriminative information for classification from continuously incoming samples. It is extremely challenging to detect novel data while updating the model in an efficient and stable fashion, especially for the chunk data. This paper proposes a fast factorization-free kernel learning method to unify novelty detection and incremental learning for unlabeled chunk data streams in one framework. The proposed method constructs a joint reproducing kernel Hilbert space from known class centers by solving a linear system in kernel space. Naturally, unlabeled data can be detected and classified among multi-classes by a single decision model. And projecting samples into the discriminative feature space turns out to be the product of two small-sized kernel matrices without needing such time-consuming factorization like QR-decomposition or singular value decomposition. Moreover, the insertion of a novel class can be treated as the addition of a new orthogonal basis to the existing feature space, resulting in fast and stable updating schemes. Both theoretical analysis and experimental validation on real-world datasets demonstrate that the proposed methods learn chunk data streams with significantly lower computational costs and comparable or superior accuracy than the state of the art.
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Jeffries, Brien, J. Wesley Hines, Albert Klein, Thomas Palmé, and Romain Bayère. "Early Detection of Boiler Leakage in a Combined Cycle Power Plant Using an Auto Associative Kernel Regression Model." In ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94216.
This paper presents the results of applying a data-driven condition-based monitoring system for the fault detection of a boiler leakage in a Combined Cycle Power Plant (CCPP). An auto associative kernel regression model is developed using normal process data and tested with faulted data to determine the earliest warning of the boiler leakage. Automatic variable grouping, which uses the linear correlations among the available thirty sensors, is employed to obtain optimal groupings to be used in model development. Several models were developed, optimized and compared. A logic test was used for fault detection and this test produced alarms in the region were the leak was later confirmed to have occurred. Comparison of these results with those of a physics-based analysis also confirmed the accuracy of the models in the early detection of the leakage.
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Omran, Ashraf, and Brett Newman. "Analytical Response for the Prototypic Nonlinear Mass-Spring-Damper System." In ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-24153.
In this paper, a procedure to analytically develop an approximate nonlinear solution for the prototypic nonlinear mass-spring-damper system based on multi-dimensional convolution expansion theory is offered. An analytical nonlinear step response is also conducted to characterize the overall system response. The developed analytical step response provides an illumination for the source of differences between nonlinear and linear responses such as initial departure time, differences in settling times and steady value, and non-symmetric response. Feasibility of the proposed implementation is assessed by a numerical example. The developed kernel-based model shows the ability to predict, understand, and analyze the system behavior beyond that attainable by linear-based model.
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Chen, Changyuan, Manases Tello Ruiz, Evert Lataire, Guillaume Delefortrie, Marc Mansuy, Tianlong Mei, and Marc Vantorre. "Ship Manoeuvring Model Parameter Identification Using Intelligent Machine Learning Method and the Beetle Antennae Search Algorithm." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95565.
Abstract In order to identify more accurately and efficiently the unknown parameters of a ship motions model, a novel Nonlinear Least Squares Support Vector Machine (NLSSVM) algorithm, whose penalty factor and Radial Basis Function (RBF) kernel parameters are optimised by the Beetle Antennae Search algorithm (BAS), is proposed and investigated. Aiming at validating the accuracy and applicability of the proposed method, the method is employed to identify the linear and nonlinear parameters of the first-order nonlinear Nomoto model with training samples from numerical simulation and experimental data. Subsequently, the identified parameters are applied in predicting the ship motion. The predicted results illustrate that the new NLSSVM-BAS algorithm can be applied in identifying ship motion’s model, and the effectiveness is verified. Compared among traditional identification approaches with the proposed method, the results display that the accuracy is improved. Moreover, the robust and stability of the NLSSVM-BAS are verified by adding noise in the training sample data.
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He, Jia, Changying Du, Changde Du, Fuzhen Zhuang, Qing He, and Guoping Long. "Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/254.
Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models seek point estimation of their parameters, and are prone to overfitting to small training data. This paper presents an adaptive kernel nonlinear max-margin multi-view learning model under the Bayesian framework. Specifically, we regularize the posterior of an efficient multi-view latent variable model by explicitly mapping the latent representations extracted from multiple data views to a random Fourier feature space where max-margin classification constraints are imposed. Assuming these random features are drawn from Dirichlet process Gaussian mixtures, we can adaptively learn shift-invariant kernels from data according to Bochners theorem. For inference, we employ the data augmentation idea for hinge loss, and design an efficient gradient-based MCMC sampler in the augmented space. Having no need to compute the Gram matrix, our algorithm scales linearly with the size of training set. Extensive experiments on real-world datasets demonstrate that our method has superior performance.
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Ippili, Rajani K., Richard D. Widdle, Patricia Davies, and Anil K. Bajaj. "Modeling and Identification of Polyurethane Foam in Uniaxial Compression: Combined Elastic and Viscoelastic Response." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/vib-48485.
Polyurethane foam used in automotive seating applications is a highly nonlinear and viscoelastic material. These properties are manifested even in its quasi-static response. In this paper, two different approaches to model and identify these material properties are presented. In both the approaches the viscoelastic property is assumed linear and modeled by a convolution of the input with a relaxation kernel that is a sum of exponentials (hereditary integral approach). The elastic force contribution is however assumed nonlinear and modeled by a polynomial in one approach, and by a model derived from Ogden strain energy function in the other. Uniaxial compression data from experiments is used to identify the parameters of the models. The robustness of the identification procedures and the issues associated with them are also discussed.
Звіти організацій з теми "Kernel linear model":
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Manninen, Terhikki, and Pauline Stenberg. Influence of forest floor vegetation on the total forest reflectance and its implications for LAI estimation using vegetation indices. Finnish Meteorological Institute, 2021. http://dx.doi.org/10.35614/isbn.9789523361379.
Recently a simple analytic canopy bidirectional reflectance factor (BRF) model based on the spectral invariants theory was presented. The model takes into account that the recollision probability in the forest canopy is different for the first scattering than the later ones. Here this model is extended to include the forest floor contribution to the total forest BRF. The effect of the understory vegetation on the total forest BRF as well as on the simple ratio (SR) and the normalized difference (NDVI) vegetation indices is demonstrated for typical cases of boreal forest. The relative contribution of the forest floor to the total BRF was up to 69 % in the red wavelength range and up to 54 % in the NIR wavelength range. Values of SR and NDVI for the forest and the canopy differed within 10 % and 30 % in red and within 1 % and 10 % in the NIR wavelength range. The relative variation of the BRF with the azimuth and view zenith angles was not very sensitive to the forest floor vegetation. Hence, linear correlation of the modelled total BRF and the Ross-thick kernel was strong for dense forests (R2 > 0.9). The agreement between modelled BRF and satellite-based reflectance values was good when measured LAI, clumping index and leaf single scattering albedo values for a boreal forest were used as input to the model.
