scholarly journals Improving the characterization of the alternative hypothesis via kernel discriminant analysis for likelihood ratio-based speaker verification

2006 ◽  
Author(s):  
Yi-Hsiang Chao ◽  
Wei-Ho Tsai ◽  
Hsin-Min Wang ◽  
Ruei-Chuan Chang
Keyword(s):  
Discriminant Analysis ◽  
Likelihood Ratio ◽  
Speaker Verification ◽  
Alternative Hypothesis ◽  
Kernel Discriminant Analysis

scholarly journals Using Kernel Discriminant Analysis to Improve the Characterization of the Alternative Hypothesis for Speaker Verification

2008 ◽  
Vol 16 (8) ◽  
pp. 1675-1684 ◽  
Author(s):  
Yi-Hsiang Chao ◽  
Wei-Ho Tsai ◽  
Hsin-Min Wang ◽  
Ruei-Chuan Chang
Keyword(s):  
Discriminant Analysis ◽  
Speaker Verification ◽  
Alternative Hypothesis ◽  
Kernel Discriminant Analysis

Exploring kernel discriminant analysis for speaker verification with limited test data

2017 ◽  
Vol 98 ◽  
pp. 26-31 ◽  
Author(s):  
Rohan Kumar Das ◽  
Akhil Babu Manam ◽  
S.R. Mahadeva Prasanna
Keyword(s):  
Discriminant Analysis ◽  
Test Data ◽  
Speaker Verification ◽  
Kernel Discriminant Analysis

Improving the characterization of the alternative hypothesis via minimum verification error training with applications to speaker verification

2009 ◽  
Vol 42 (7) ◽  
pp. 1351-1360
Author(s):  
Yi-Hsiang Chao ◽  
Wei-Ho Tsai ◽  
Hsin-Min Wang ◽  
Ruei-Chuan Chang
Keyword(s):  
Speaker Verification ◽  
Alternative Hypothesis ◽  
Error Training

Regression model of assessment of customer solvency and banking risks in the process of lending

2019 ◽  
pp. 69-73
Author(s):  
Zoryna Yurynets ◽  
Rostyslav Yurynets ◽  
Nataliya Kunanets ◽  
Ivanna Myshchyshyn
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Regression Model ◽  
Likelihood Ratio ◽  
Credit Scoring ◽  
Bank Loans ◽  
Management Decisions ◽  
Computer Data ◽  
Proposed Model ◽  
Loan Risk

In the current conditions of economic development, it is important to pay attention to the study of the main types of risks, effective methods of evaluation, monitoring, analysis of banking risks. One of the main approaches to quantitatively assessing the creditworthiness of borrowers is credit scoring. The objective of credit scoring is to optimize management decisions regarding the possibility of providing bank loans. In the article, the scientific and methodological provisions concerning the formation of a regression model for assessing bank risks in the process of granting loans to borrowers has been proposed. The proposed model is based on the use of logistic regression tools, discriminant analysis with the use of expert evaluation. During the formation of a regression model, the relationship between risk factors and probable magnitude of loan risk has been established. In the course of calculations, the coefficient of the individual's solvency has been calculated. Direct computer data preparation, including the calculation of the indicators selected in the process of discriminant analysis, has been carried out in the Excel package environment, followed by their import into the STATISTICA package for analysis in the “Logistic regression” sub-module of the “Nonlinear evaluation” module. The adequacy of the constructed model has been determined using the Macfaden's likelihood ratio index. The calculated value of the Macfaden's likelihood ratio index indicates the adequacy of the constructed model. The ability to issue loans to new clients has been evaluated using a regression model. The conducted calculations show the possibility of granting a loan exclusively to the second and third clients. The offered method allows to conduct assessment of client's solvency and risk prevention at different stages of lending, facilitates the possibility to independently make informed decisions on credit servicing of clients and management of a loan portfolio, optimization of management decisions in banks. In order for a loan-based model to continue to perform its functions, it must be periodically adjusted.

scholarly journals Kernel discriminant analysis and clustering with parsimonious Gaussian process models

2014 ◽  
Vol 25 (6) ◽  
pp. 1143-1162 ◽  
Author(s):  
C. Bouveyron ◽  
M. Fauvel ◽  
S. Girard
Keyword(s):  
Discriminant Analysis ◽  
Gaussian Process ◽  
Process Models ◽  
Kernel Discriminant Analysis ◽  
Gaussian Process Models

scholarly journals Asymptotic relative submajorization of multiple-state boxes

2021 ◽  
Vol 111 (4) ◽  
Author(s):  
Gergely Bunth ◽  
Péter Vrana
Keyword(s):  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Alternative Hypothesis ◽  
Standard Form ◽  
Point Of View ◽  
State Discrimination ◽  
Simple Alternative ◽  
Multiple State ◽  
Error Probabilities

AbstractPairs of states, or “boxes” are the basic objects in the resource theory of asymmetric distinguishability (Wang and Wilde in Phys Rev Res 1(3):033170, 2019. 10.1103/PhysRevResearch.1.033170), where free operations are arbitrary quantum channels that are applied to both states. From this point of view, hypothesis testing is seen as a process by which a standard form of distinguishability is distilled. Motivated by the more general problem of quantum state discrimination, we consider boxes of a fixed finite number of states and study an extension of the relative submajorization preorder to such objects. In this relation, a tuple of positive operators is greater than another if there is a completely positive trace nonincreasing map under which the image of the first tuple satisfies certain semidefinite constraints relative to the other one. This preorder characterizes error probabilities in the case of testing a composite null hypothesis against a simple alternative hypothesis, as well as certain error probabilities in state discrimination. We present a sufficient condition for the existence of catalytic transformations between boxes, and a characterization of an associated asymptotic preorder, both expressed in terms of sandwiched Rényi divergences. This characterization of the asymptotic preorder directly shows that the strong converse exponent for a composite null hypothesis is equal to the maximum of the corresponding exponents for the pairwise simple hypothesis testing tasks.

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