Error Exponents for Target-Class Detection with Nuisance Parameters

2007 ◽  
Author(s):  
Saswat Misra ◽  
Lang Tong
Keyword(s):  
Nuisance Parameters ◽  
Target Class ◽  
Error Exponents

Error Exponents for Target-Class Detection in a Sensor Network

MILCOM 2006 ◽  
2006 ◽  
Author(s):  
Saswat Misra ◽  
Lang Tong ◽  
Anthony Ephremides
Keyword(s):  
Sensor Network ◽  
Target Class ◽  
Error Exponents

Is That Measure Really One-Dimensional?

Methodology ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 188-196 ◽  
Author(s):  
Esther T. Beierl ◽  
Markus Bühner ◽  
Moritz Heene
Keyword(s):  
Model Fit ◽  
Nuisance Parameters ◽  
Factorial Validity ◽  
Fit Indices ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
One Dimensional ◽  
Confirmatory Factor Analysis Model ◽  
Cutoff Values ◽  
Confirmatory Factor

Abstract. Factorial validity is often assessed using confirmatory factor analysis. Model fit is commonly evaluated using the cutoff values for the fit indices proposed by Hu and Bentler (1999) . There is a body of research showing that those cutoff values cannot be generalized. Model fit does not only depend on the severity of misspecification, but also on nuisance parameters, which are independent of the misspecification. Using a simulation study, we demonstrate their influence on measures of model fit. We specified a severe misspecification, omitting a second factor, which signifies factorial invalidity. Measures of model fit showed only small misfit because nuisance parameters, magnitude of factor loadings and a balanced/imbalanced number of indicators per factor, also influenced the degree of misfit. Drawing from our results, we discuss challenges in the assessment of factorial validity.

IMPLEMENTASI MANAJEMEN PEMBELAJARAN TAHFIDZUL QUR’AN DI MI ISTIQOMAH SAMBAS PURBALINGGA

2018 ◽  
Vol 19 (2) ◽  
pp. 104-120
Author(s):  
Maulana Khusen
Keyword(s):  
Decision Making ◽  
Teacher Performance ◽  
Learning Program ◽  
Target Class ◽  
Class 1 ◽  
Second Year ◽  
Learning Management ◽  
Coordination Meeting

Abstract: The results of the study show that: (1) Tahfidzul Qur'an learning planning is done through the preparation of memorization targets and the determination of effective weeks and days in each semester; (2) Organizing is carried out through the division of tasks and responsibilities as well as the construction of the structure of the tutoring teacher; (3) The mobilization is carried out through the coordination meeting of the Tahfidz coordinator as a shering forum for decision making and direction of the Tahfidzul Qur'an learning program and the implementation of learning is carried out every Monday-Friday; and (4) Supervision is carried out through assessing teacher performance at the end of December and June. The highest achievement target for the second year of the implementation of the Tahfidzul Qur'an's 2017/2018 year program is juz 29 and 30, the lowest target for class 1 is juz 30 to Surat al Ghosyiyyah. For class 1, 85% of the target is achieved and 11% of students exceed the target. Class 2 targets reached 19%. Class 3, 10.86% reached the target and 0.35% of students exceeded the target. Class 4 tarjet reached 12.44%. Class 5 targets reached 4.24%, and the last grade 6 target reached 13.79% and 1.5% of students exceeded the target. Keywords: Learning Management, Tahfidzul Qur'an.

scholarly journals Meta Learning for Few-Shot One-Class Classification

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 195-208
Author(s):  
Gabriel Dahia ◽  
Maurício Pamplona Segundo
Keyword(s):  
Feature Representation ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Target Class ◽  
Feature Representations ◽  
Shot Classification ◽  
Training Stage ◽  
Comparable Performance ◽  
Meta Learning ◽  
One Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

On procrustean mean shapes and the shape of the means

1996 ◽  
Vol 28 (2) ◽  
pp. 336-337
Author(s):  
Hulling Le
Keyword(s):  
Statistical Models ◽  
Nuisance Parameters ◽  
Finite Set

Two sets of k labelled points, or configurations, in ℝm are defined to have the same shape if they differ only in translation, rotation and scaling. An important matter in practice is the estimation of the shape of the means; the shape determined by the means of data on the vertices of configurations. However, statistical models for vertices-based shapes always involve some unknown samplewise nuisance parameters associated with ambiguity of location, rotation and scaling. The use of procrustean mean shapes for a finite set of configurations, which are usually formulated directly in terms of their vertices, will enable one to eliminate these nuisance parameters.

scholarly journals Mapping Panax Notoginseng Plantations by Using an Integrated Pixel- and Object-Based (IPOB) Approach and ZY-3 Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2184
Author(s):  
Zhiqi Yang ◽  
Jinwei Dong ◽  
Weili Kou ◽  
Yuanwei Qin ◽  
Xiangming Xiao
Keyword(s):  
Spatial Distribution ◽  
Yunnan Province ◽  
Vascular Diseases ◽  
Panax Notoginseng ◽  
Traditional Herbal Medicine ◽  
Target Class ◽  
Object Based ◽  
The Government ◽  
Level Scale ◽  
Iteration Analysis

Plantations of Panax notoginseng (PN), traditional herbal medicine for the prevention and treatment of vascular diseases, are expanding rapidly in China, especially in the Yunnan province of China, due to its increasing demands and prices and causing dramatic environmental concerns. However, existing information on its planting area and spatial distribution are limited. Here, we mapped the PN planting area by using a new integrated pixel- and object-based (IPOB) approach, the Random Forest (RF) classifier, and the high-resolution ZiYuan-3 (ZY-3) imagery. We improved the procedures of classification in three aspects: (1) a new spectral index—Normalized Difference PN Index (NDPI)—was proposed, (2) the efficiency and scale of segmentation were optimized by using the Bi-level Scale-sets Model (BSM), and (3) feature variables were selected through an iteration analysis from 99 feature variables (spectral, textural, geometric, and geographic). Compared with the pixel- and the object-based methods, the IPOB has the highest F1 score of 0.98 and also has high robustness in terms of user and producer accuracies (97% and 99%, respectively), following by the object-based method (F1 = 0.94) and the pixel-based method (F1 = 0.93). The high accuracy was expected since the target class has very distinctive spectral and textural characteristics. Although all three approaches showed reasonably high accuracies due to the application of the NDPI and optimized procedures, the result showed the outperformance of the proposed IPOB approach. The framework established in this study expects to apply for regional or national PN surveys extensively. The information on the area and spatial distribution of PN can guide the government on policy making for the planting and exporting of traditional Chinese medicine resources.

scholarly journals A simple solution to the inadequacy of asymptotic likelihood-based inference for response-adaptive clinical trials

2021 ◽  
Author(s):  
Alessandro Baldi Antognini ◽  
Marco Novelli ◽  
Maroussa Zagoraiou
Keyword(s):  
Clinical Trials ◽  
Primary Outcome ◽  
Wald Test ◽  
Simple Solution ◽  
Nuisance Parameters ◽  
Simulation Studies ◽  
Fundamental Properties ◽  
Adaptive Clinical Trials ◽  
Treatment Comparisons ◽  
Practical Recommendations

AbstractThe present paper discusses drawbacks and limitations of likelihood-based inference in sequential clinical trials for treatment comparisons managed via Response-Adaptive Randomization. Taking into account the most common statistical models for the primary outcome—namely binary, Poisson, exponential and normal data—we derive the conditions under which (i) the classical confidence intervals degenerate and (ii) the Wald test becomes inconsistent and strongly affected by the nuisance parameters, also displaying a non monotonic power. To overcome these drawbacks, we provide a very simple solution that could preserve the fundamental properties of likelihood-based inference. Several illustrative examples and simulation studies are presented in order to confirm the relevance of our results and provide some practical recommendations.

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