Extraction of Hierarchical Behavior Patterns Using a Non-parametric Bayesian Approach
نویسندگان
چکیده
منابع مشابه
Binary Classifier Calibration Using a Bayesian Non-Parametric Approach
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are ind...
متن کاملA Bayesian Approach for the Recognition of Control Chart Patterns
In this research, an iterative approach is employed to recognize and classify control chart patterns. To do this, by taking new observations on the quality characteristic under consideration, the Maximum Likelihood Estimator of pattern parameters is first obtained and then the probability of each pattern is determined. Then using Bayes’ rule, probabilities are updated recursively. Finally, when...
متن کاملA Hybrid Parametric, Non-parametric Approach to Bayesian Target Tracking
This article describes a versatile approach to non-linear, non-Gaussian noise target tracking which makes use of both parametric and non-parametric techniques within a Bayesian framework. It produces a Gaussian mixture model (GMM) of a track, but resorts to a sampling technique within the tracking process to handle non-linearity. GMMs are recovered from samples using the expectation-maximisatio...
متن کاملBinary Classifier Calibration: Bayesian Non-Parametric Approach
A set of probabilistic predictions is well calibrated if the events that are predicted to occur with probability p do in fact occur about p fraction of the time. Well calibrated predictions are particularly important when machine learning models are used in decision analysis. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method bas...
متن کاملClassification Using a Hierarchical Bayesian Approach
A key problem faced by classifiers is coping with styles not represented in the training set. We present an application of hierarchical Bayesian methods to the problem of recognizing degraded printed characters in a variety of fonts. The proposed method works by using training data of various styles and classes to compute prior distributions on the parameters for the class conditional distribut...
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: Frontiers in Computer Science
سال: 2020
ISSN: 2624-9898
DOI: 10.3389/fcomp.2020.546917