SINFA: Multivariate uncertainty analysis for confusion matrices
نویسندگان
چکیده
منابع مشابه
Analysis of tactile and visual confusion matrices.
Confusion matrices were compiled for uppercase letters and for braille characters presented to observers in two ways: as raised touch stimuli and as visual stimuli that had been optically filtered of their higher spatial frequencies. These and other existing matrices were subjected to a number of analyses, including the choice model and hierarchical clustering. The strong similarity of the visu...
متن کاملInformation Covariance Matrices for Multivariate Burr III and Logistic Distributions
Main result of this paper is to derive the exact analytical expressions of information and covariance matrices for multivariate Burr III and logistic distributions. These distributions arise as tractable parametric models in price and income distributions, reliability, economics, Human population, some biological organisms to model agricultural population data and survival data. We showed that ...
متن کاملDetecting Features from Confusion Matrices Using Generalized Formal Concept Analysis
We claim that the confusion matrices of multiclass problems can be analyzed by means of a generalization of Formal Concept Analysis to obtain symbolic information about the feature sets of the underlying classification task. We prove our claims by analyzing the confusion matrices of human speech perception experiments and comparing our results to those elicited by experts.
متن کاملCalibration and Uncertainty Analysis for Computer Simulations with Multivariate Output
Model calibration entails the inference about unobservable modeling parameters based on experimental observations of system response. When the model being calibrated is an expensive computer simulation, special techniques such as surrogate modeling and Bayesian inference are often fruitful. In this work we show how the flexibility of the Bayesian calibration approach can be exploited in order t...
متن کاملA Multivariate Input Uncertainty in Output Analysis for Stochastic Simulation
When we use simulations to estimate the performance of stochastic systems, the simulation is often driven by input models estimated from finite real-world data. A complete statistical characterization of system performance estimates requires quantifying both input model and simulation estimation errors. The components of input models in many complex systems could be dependent. In this paper, we...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Behavior Research Methods & Instrumentation
سال: 1976
ISSN: 1554-351X,1554-3528
DOI: 10.3758/bf03202187