Parameter estimation in abruptly changing dynamic environments using stochastic learning weak estimator
Many real-life dynamical systems experience abrupt changes followed by almost stationary
periods. In this paper, we consider streams of data exhibiting such abrupt behavior and …
periods. In this paper, we consider streams of data exhibiting such abrupt behavior and …
An adaptive estimation method with exploration and exploitation modes for non-stationary environments
Dynamic systems are highly complex and hard to deal with due to their subject-and time-
varying nature. The fact that most of the real world systems/events are of dynamic character …
varying nature. The fact that most of the real world systems/events are of dynamic character …
Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments
The task of designing estimators that are able to track time-varying distributions has found
promising applications in many real-life problems. Existing approaches resort to sliding …
promising applications in many real-life problems. Existing approaches resort to sliding …
Detection of regime switching points in non-stationary sequences using stochastic learning based weak estimation method
In general, dynamic systems are systems with time-dependent behavior. Dynamic systems
are characterized by the non-stationary data sequences they emit. One particular way to …
are characterized by the non-stationary data sequences they emit. One particular way to …
Novel discretized weak estimators based on the principles of the stochastic search on the line problem
Generally speaking, research in the field of estimation involves designing strong estimators,
ie, those which converge with probability 1, as the number of samples increases indefinitely …
ie, those which converge with probability 1, as the number of samples increases indefinitely …
A novel stochastic discretized weak estimator operating in non-stationary environments
The task of designing estimators that are able to track time-varying distributions has found
promising applications in many real-life problems. A particularly interesting family of …
promising applications in many real-life problems. A particularly interesting family of …
On the online classification of data streams using weak estimators
In this paper, we propose a novel online classifier for complex data streams which are
generated from non-stationary stochastic properties. Instead of using a single training model …
generated from non-stationary stochastic properties. Instead of using a single training model …
A stochastic search on the line-based solution to discretized estimation
Abstract Recently, Oommen and Rueda [11] presented a strategy by which the parameters
of a binomial/multinomial distribution can be estimated when the underlying distribution is …
of a binomial/multinomial distribution can be estimated when the underlying distribution is …
On utilizing stochastic learning weak estimators for training and classification of patterns with non-stationary distributions
Pattern recognition essentially deals with the training and classification of patterns, where
the distribution of the features is assumed unknown. However, in almost all the reported …
the distribution of the features is assumed unknown. However, in almost all the reported …
On utilizing weak estimators to achieve the online classification of data streams
Classification, typically, deals with unique and distinct training and testing phases. This
paper pioneers the concept when these phases are not so clearly well-defined. More …
paper pioneers the concept when these phases are not so clearly well-defined. More …