This paper presents a new framework for dealing with two main types of concept drift (sudden and gradual) in labeled data with decision attribute. The learning examples are processed instance by instance. This new framework, called Online Batch Weighted Ensemble, introduces element of incremental processing into a block-based ensemble of classi ers. Its performance was evaluated experimentally ...

We show that there is no batch-means estimation procedure for consistently estimating the asymptotic variance when the number of batches is held fixed as the run length increases. This result suggests that the number of batches should increase as the run length increases for sequential stopping rules based on batch means.

We present a new method for obtaining confidence intervals in steady-state simulation. In our replicated batch means method, we do a small number of independent replications to estimate the steady-state mean of the underlying stochastic process. In order to obtain a variance estimator, we further group the observations from these replications into nonoverlapping batches. We show that for large ...

We propose a new learning to rank algorithm, named Weighted Margin-Rank Batch loss (WMRB), to extend the popular Weighted Approximate-Rank Pairwise loss (WARP). WMRB uses a new rank estimator and an efficient batch training algorithm. The approach allows more accurate item rank approximation and explicit utilization of parallel computation to accelerate training. In three item recommendation ta...