Learning in Non-stationary Environments
نویسنده
چکیده
منابع مشابه
The Time Adaptive Self Organizing Map for Distribution Estimation
The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...
متن کاملمکان یابی وفقی موبایل به روش آزمون باقیمانده
Determination of mobile localization with time of arrival (TOA) signal is a requirement in cellular mobile communication. In some of the previous methods, localization with non-line-of-sight (NLOS) paths can lead to large position error. Also for simplicity, in most simulations suppose non stationary actual environments as stationary. This paper proposes (residual test + recursive least square)...
متن کاملAddressing Environment Non-Stationarity by Repeating Q-learning Updates
Q-learning (QL) is a popular reinforcement learning algorithm that is guaranteed to converge to optimal policies in Markov decision processes. However, QL exhibits an artifact: in expectation, the effective rate of updating the value of an action depends on the probability of choosing that action. In other words, there is a tight coupling between the learning dynamics and underlying execution p...
متن کاملLearning to Negotiate Optimally in Non-stationary Environments
We adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal strategy in incomplete information settings. Specifically, an agent learns the optimal strategy to play against an opponent whose strategy varies with time, assuming no prior information about its negotiation parameters. In so doin...
متن کاملClassifier Ensembles for Changing Environments
We consider strategies for building classifier ensembles for non-stationary environments where the classification task changes during the operation of the ensemble. Individual classifier models capable of online learning are reviewed. The concept of “forgetting” is discussed. Online ensembles and strategies suitable for changing environments are summarized.
متن کامل