Dynamic Programming for Bayesian Logistic Regression Learning under Concept Drift
نویسندگان
چکیده
A data stream is an ordered sequence of training instances arriving at a rate that does not permit to permanently store them in memory and leads to the necessity of online learning methods when trying to predict some hidden target variable. In addition, concept drift often occurs, what means means that the statistical properties of the target variable may change over time. In this paper, we present a framework of solving the online pattern recognition problem in data streams under concept drift. The framework is based on the application of the Bayesian approach to the probabilistic pattern recognition model in terms of logistic regression, hidden Markov model and dynamic programming.
منابع مشابه
Learning from Data Streams with Concept Drift
Increasing access to incredibly large, nonstationary datasets and corresponding demands to analyse these data has led to the development of new online algorithms for performing machine learning on data streams. An important feature of real-world data streams is " concept drift, " whereby the distributions underlying the data can change arbitrarily over time. The presence of concept drift in a d...
متن کاملCost Analysis of Acceptance Sampling Models Using Dynamic Programming and Bayesian Inference Considering Inspection Errors
Acceptance Sampling models have been widely applied in companies for the inspection and testing the raw material as well as the final products. A number of lots of the items are produced in a day in the industries so it may be impossible to inspect/test each item in a lot. The acceptance sampling models only provide the guarantee for the producer and consumer that the items in the lots are acco...
متن کاملSample size determination for logistic regression
The problem of sample size estimation is important in medical applications, especially in cases of expensive measurements of immune biomarkers. This paper describes the problem of logistic regression analysis with the sample size determination algorithms, namely the methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regr...
متن کاملModel Averaging via Penalized Regression for Tracking Concept Drift
A supervised learning algorithm aims to build a prediction model using training examples. This paradigm typically has the assumptions that the underlying distribution and the true input-output dependency does not change. However, these assumptions often fail to hold, especially in data streams. This phenomenon is known as concept drift. We propose a new model combining algorithm for tracking co...
متن کاملZhuSuan: A Library for Bayesian Deep Learning
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan is featured for its deep root into Bayesia...
متن کامل