scholarly journals Statistical Classification of the Observation of Nuggetless Spatial Gaussian Process with Unknown Sill Parameter

2009 ◽  
Vol 14 (2) ◽  
pp. 155-163 ◽  
Author(s):  
K. Dučinskas
Keyword(s):  
Asymptotic Expansion ◽  
Gaussian Process ◽  
Discriminant Function ◽  
Training Sample ◽  
Unknown Parameters ◽  
Process Observation ◽  
Two Populations ◽  
Sample Case ◽  
Accuracy Of Approximation

The problem of classification of spatial Gaussian process observation into one of two populations specified by different regression mean models and common stationary covariance with unknown sill parameter is considered. Unknown parameters are estimated from training sample and these estimators are plugged in the Bayes discriminant function. The asymptotic expansion of the expected error rate associated with Bayes plug-in discriminant function is derived. Numerical analysis of the accuracy of approximation based on derived asymptotic expansion in the small training sample case is carried out. Comparison of two spatial sampling designs based on values of this approximation is done.

scholarly journals The influence of training sampling size on the expected error rate in spatial classification

2012 ◽  
Vol 53 ◽  
Author(s):  
Lina Dreižienė ◽  
Marta Karaliutė
Keyword(s):  
Discriminant Function ◽  
Error Rate ◽  
Asymptotic Approximation ◽  
Covariance Function ◽  
Spatial Sampling ◽  
Anisotropy Ratio ◽  
Gaussian Data ◽  
Two Populations ◽  
Bayes Discriminant Function

In this paper we use the pluged-in Bayes discriminant function (PBDF) for classification of spatial Gaussian data into one of two populations specified by different parametric mean models and common geometric anisotropic covariance function. The pluged-in Bayes discriminant function is constructed by using ML estimators of unknown mean and anisotropy ratio parameters. We focus on the asymptotic approximation of expected error rate (AER) and our aim is to investigate the effects of two different spatial sampling designs (based on increasing and fixed domain asymptotics) on AER.

scholarly journals Discriminant analysis of the equicorrelated Gaussian observations

2021 ◽  
Vol 47 ◽  
Author(s):  
Kęstutis Dučinskas ◽  
Jurgita Neverdauskaiė
Keyword(s):  
Numerical Analysis ◽  
Discriminant Analysis ◽  
Maximum Likelihood ◽  
Discriminant Function ◽  
Error Rate ◽  
Maximum Likelihood Estimators ◽  
Training Sample ◽  
Common Variance ◽  
Bayes Discriminant Function

In this paper the problem of classification of an observation into one of two Gaussian populations with different means and common variance is considered in the case when equicorrelated training sample is given. Unknown means and common variance are estimated from training sample and these estimators are pluged in the Bayes discriminant function. The maximum likelihood estimators are used. The approximation of the expected error rate associated with Bayes plug-in discriminant function is derived. Numerical analysis of the accuracy of that approximation for various values of correlation is presented.

Paepalanthus fabianeae (Eriocaulaceae): A New Microendemic Species from a Morphologically Coherent Clade

2020 ◽  
Vol 45 (4) ◽  
pp. 794-801
Author(s):  
Caroline Oliveira Andrino ◽  
Marcelo Fragomeni Simon ◽  
Jair Eustáquio Quintino Faria ◽  
André Luiz da Costa Moreira ◽  
Paulo Takeo Sano
Keyword(s):  
New Species ◽  
Iucn Red List ◽  
Phylogenetic Position ◽  
Similar Species ◽  
Infrageneric Classification ◽  
Espinhaço Range ◽  
Nuclear Its ◽  
Two Populations

Abstract—We describe and illustrate Paepalanthus fabianeae, a new species of Eriocaulaceae from the central portion of the Espinhaço Range in Minas Gerais, Brazil. Previous phylogenetic evidence based on analyses of nuclear (ITS and ETS) and plastid (trnL-trnF and psba-trnH) sequences revealed P. fabianeae as belonging to a strongly supported and morphologically coherent clade containing five other species, all of them microendemic, restricted to the Espinhaço range. Due to the infrageneric classification of Paepalanthus being highly artificial, we preferred not assigning P. fabianeae to any infrageneric group. Paepalanthus fabianeae is known from two populations growing in campos rupestres (highland rocky fields) in the meridional Espinhaço Range. The species is characterized by pseudodichotomously branched stems, small, linear, recurved, and reflexed leaves, urceolate capitula, and bifid stigmas. Illustrations, photos, the phylogenetic position, and a detailed description, as well as comments on habitat, morphology, and affinities with similar species are provided. The restricted area of occurrence allied with threats to the quality of the habitat, mainly due to quartzite mining, justifies the preliminary classification of the new species in the Critically Endangered (CR) category using the guidelines and criteria of the IUCN Red List.

EM Algorithm Based on Fuzzification and its Application

2012 ◽  
Vol 532-533 ◽  
pp. 1445-1449
Author(s):  
Ting Ting Tong ◽  
Zhen Hua Wu
Keyword(s):  
Em Algorithm ◽  
Mixed Model ◽  
Image Data ◽  
Training Sample ◽  
Model Parameters ◽  
Parameter Learning ◽  
Weighted Degree ◽  
The Em Algorithm ◽  
The Impact

EM algorithm is a common method to solve mixed model parameters in statistical classification of remote sensing image. The EM algorithm based on fuzzification is presented in this paper to use a fuzzy set to represent each training sample. Via the weighted degree of membership, different samples will be of different effect during iteration to decrease the impact of noise on parameter learning and to increase the convergence rate of algorithm. The function and accuracy of classification of image data can be completed preferably.

Classification method of marine target motion pattern based on spatial-temporal trajectories

2021 ◽  
pp. 1-15
Author(s):  
Baichen Jiang ◽  
Wei Zhou ◽  
Jian Guan ◽  
Jialong Jin
Keyword(s):  
Pattern Classification ◽  
Training Sample ◽  
Classification Algorithm ◽  
Classification Model ◽  
Motion Pattern ◽  
Motion Patterns ◽  
Lp Norm ◽  
Sparse Representation Classification ◽  
Route Safety

Classifying the motion pattern of marine targets is of important significance to promote target surveillance and management efficiency of marine area and to guarantee sea route safety. This paper proposes a moving target classification algorithm model based on channel extraction-segmentation-LCSCA-lp norm minimization. The algorithm firstly analyzes the entire distribution of channels in specific region, and defines the categories of potential ship motion patterns; on this basis, through secondary segmentation processing method, it obtains several line segment trajectories as training sample sets, to improve the accuracy of classification algorithm; then, it further uses the Leastsquares Cubic Spline Curves Approximation (LCSCA) technology to represent the training sample sets, and builds a motion pattern classification sample dictionary; finally, it uses lp norm minimized sparse representation classification model to realize the classification of motion patterns. The verification experiment based on real spatial-temporal trajectory dataset indicates that, this method can effectively realize the motion pattern classification of marine targets, and shows better time performance and classification accuracy than other representative classification methods.

Validity and Clinical Scale Comparisons between the MMPI and MMPI-2 with Psychiatric Inpatients

1992 ◽  
Vol 70 (1) ◽  
pp. 323-332 ◽  
Author(s):  
Dudley David Blake ◽  
Phillip M. Kleespies ◽  
Walter E. Penk ◽  
Suellen S. Walsh ◽  
DeAnna L. Mori ◽  
...  
Keyword(s):  
Discriminant Function ◽  
Relative Stability ◽  
Patient Population ◽  
Psychiatric Patients ◽  
Special Populations ◽  
Clinical Scale ◽  
Acute Psychiatry ◽  
Highly Correlated ◽  
Discriminant Function Analyses

This study was designed to investigate the comparability of the original MMPI (1950) and the MMPI-2 (1989) with a psychiatric patient population. 34 male and 3 female patients, shortly after admission to one of two acute psychiatry units, completed the old and revised versions of the MMPI. Paired t tests indicated but scant differences for raw scores, while many more differences were found among T scores for validity, clinical, and supplemental scales. Analyses, however, showed all scales on the two forms to be highly correlated. Analysis of the high-point and two-point codes across the two administrations also showed relative stability, although the proportion of Scales 2 (Depression) and 8 (Schizophrenia) decreased, while those for Scales 6 (Paranoia) and 7 (Psychasthenia) increased markedly in the MMPI-2 protocols. Examination of each version's discriminability among mood- and thought-disordered subsamples suggested that the MMPI provides slightly better delineation between diagnostic classes. Discriminant function analyses showed that there were essentially no differences between the two forms in the accurate classification of clinical and nonclinical groups. The findings reported here provide support for the MMPI-2; despite modification, the newer form retains the advantages of the original MMPI. Differences found here may be unique to psychiatric patients and their patterns of MMPI/MMPI-2 equivalence and may not generalize to other special populations.

scholarly journals Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions

2018 ◽  
Vol 30 (11) ◽  
pp. 3072-3094 ◽  
Author(s):  
Hongqiao Wang ◽  
Jinglai Li
Keyword(s):  
Bayesian Inference ◽  
Gaussian Process ◽  
Adaptive Algorithm ◽  
Posterior Density ◽  
Unknown Parameters ◽  
Likelihood Functions ◽  
Inference Problems ◽  
Active Learning Method ◽  
Computationally Intensive ◽  
Process Approximation

We consider Bayesian inference problems with computationally intensive likelihood functions. We propose a Gaussian process (GP)–based method to approximate the joint distribution of the unknown parameters and the data, built on recent work (Kandasamy, Schneider, & Póczos, 2015 ). In particular, we write the joint density approximately as a product of an approximate posterior density and an exponentiated GP surrogate. We then provide an adaptive algorithm to construct such an approximation, where an active learning method is used to choose the design points. With numerical examples, we illustrate that the proposed method has competitive performance against existing approaches for Bayesian computation.

scholarly journals Nonlinear Classification of EEG recordings from patients with Alzheimer’s Disease using Gaussian Process Latent Variable Model

2020 ◽  
Author(s):  
S. Rajintha. A. S. Gunawardena ◽  
Fei He ◽  
Ptolemaios Sarrigiannis ◽  
Daniel J. Blackburn
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gaussian Process ◽  
Manifold Learning ◽  
Latent Variable ◽  
Time Window ◽  
Support Vector ◽  
Nonlinear Dimensionality Reduction ◽  
Variable Model

AbstractIn this work, nonlinear temporal features from multi-channel EEGs are used for the classification of Alzheimer’s disease patients from healthy individuals. This was achieved by temporal manifold learning using Gaussian Process Latent Variable Models (GPLVM) as a nonlinear dimensionality reduction technique. Classification of the extracted features was undertaken using a nonlinear Support Vector Machine. Comparisons were made against the linear counterpart, Principle Component Analysis while exploring the effect of the time window or EEG epoch length used. It was demonstrated that temporal manifold learning using GPLVM is better in extracting features that attain high separability and prediction accuracy. This work aims to set the significance of using GPLVM temporal manifold learning for EEG feature extraction in the classification of Alzheimer’s disease.

scholarly journals Selected Robust Logistic Regression Specification for Classification of Multi‑dimensional Functional Data in Presence of Outlier

2018 ◽  
Vol 2 (334) ◽  
Author(s):  
Mirosław Krzyśko ◽  
Łukasz Smaga
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Functional Data ◽  
Logistic Regression Model ◽  
Binary Classification ◽  
Classification Problem ◽  
Classification Rule ◽  
Unknown Parameters ◽  
Explanatory Variables

In this paper, the binary classification problem of multi‑dimensional functional data is considered. To solve this problem a regression technique based on functional logistic regression model is used. This model is re‑expressed as a particular logistic regression model by using the basis expansions of functional coefficients and explanatory variables. Based on re‑expressed model, a classification rule is proposed. To handle with outlying observations, robust methods of estimation of unknown parameters are also considered. Numerical experiments suggest that the proposed methods may behave satisfactory in practice.

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