Many real-life dynamical systems change abruptly followed by almost stationary periods. In this paper, we consider streams of data with such abrupt behavior and investigate the problem of tracking their statistical properties in an online manner. We devise a tracking procedure where an estimator that is suitable for a stationary environment is combined together with an event detection method such that the estimator rapidly can jump to a more suitable value if an event is detected. Combining an estimation procedure with detection procedure is commonly known idea in the literature. However, our contribution lies in building the detection procedure based on the difference between the stationary estimator and a Stochastic Learning Weak Estimator (SLWE). The SLWE estimator is known to be the state-of-the art approach to tracking properties of non-stationary environments and thus should be a better choice to detect changes in abruptly changing environments than the far more common sliding window based approaches. To the best of our knowledge, the event detection procedure suggested by (2012) is the only procedure in the literature taking advantage of the powerful tracking properties of the SLWE estimator. The procedure in is however quite complex and not well founded theoretically compared to the procedures in this paper. In this paper, we focus on estimation procedure for the binomial and multinomial distributions, but our approach can be easily generalized to cover other distributions as well. Extensive simulation results based on both synthetic and real-life data related to news classification demonstrate that our estimation procedure is easy to tune and performs well.
Gama . @cite_18 presents a clear distinction between memory management and forgetting mechanisms. Adaptive windowing @cite_27 works with the premise of growing the size the sliding window indefinitely until a change is detected via a change detection technique. In this situation, the size of the window is reset whenever a changed is detected.
@cite_27 @cite_18
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This paper presents a theoretical study on distributed contention window control algorithms for achieving arbitrary bandwidth allocation policies and efficient channel utilization. By modeling different bandwidth allocation policies as an optimal contention window assignment problem, the authors design a general and fully distributed contention window control algorithm, called general contention window adaptation (GCA), and prove that it converges to the solution of the contention window assignment problem. By examining the stability of GCA, we identify the optimal stable point that maximizes channel utilization and provide solutions to control the stable point near the optimal point. Due to the generality of GCA, this work provides a theoretical foundation to analyze existing and design new contention window control algorithms. We consider the estimation of an independent and identically distributed (i.i.d.) (possibly non-Gaussian) vector x ∈ Rn from measurements y ∈ Rm obtained by a general cascade model consisting of a known linear transform followed by a probabilistic componentwise (possibly nonlinear) measurement channel. A novel method, called adaptive generalized approximate message passing (adaptive GAMP) is presented. It enables the joint learning of the statistics of the prior and measurement channel along with estimation of the unknown vector x. We prove that, for large i.i.d. Gaussian transform matrices, the asymptotic componentwise behavior of the adaptive GAMP is predicted by a simple set of scalar state evolution equations. In addition, we show that the adaptive GAMP yields asymptotically consistent parameter estimates, when a certain maximum-likelihood estimation can be performed in each step. This implies that the algorithm achieves a reconstruction quality equivalent to the oracle algorithm that knows the correct parameter values. Remarkably, this result applies to essentially arbitrary parametrizations of the unknown distributions, including nonlinear and non-Gaussian ones. The adaptive GAMP methodology thus provides a systematic, general and computationally efficient method applicable to a large range of linear-nonlinear models with provable guarantees. We present a discriminative online algorithm with a bounded memory growth, which is based on the kernel-based Perceptron. Generally, the required memory of the kernel-based Perceptron for storing the online hypothesis is not bounded. Previous work has been focused on discarding part of the instances in order to keep the memory bounded. In the proposed algorithm the instances are not discarded, but projected onto the space spanned by the previous online hypothesis. We derive a relative mistake bound and compare our algorithm both analytically and empirically to the state-of-the-art Forgetron algorithm (, 2007). The first variant of our algorithm, called Projectron, outperforms the Forgetron. The second variant, called Projectron++, outperforms even the Perceptron. Memory management system performance is of increasing importance in today's managed languages. Two lingering sources of overhead are the direct costs of memory allocations and write barriers. This paper introduces it allocation folding, an optimization technique where the virtual machine automatically folds multiple memory allocation operations in optimized code together into a single, larger it allocation group. An allocation group comprises multiple objects and requires just a single bounds check in a bump-pointer style allocation, rather than a check for each individual object. More importantly, all objects allocated in a single allocation group are guaranteed to be contiguous after allocation and thus exist in the same generation, which makes it possible to statically remove write barriers for reference stores involving objects in the same allocation group. Unlike object inlining, object fusing, and object colocation, allocation folding requires no special connectivity or ownership relation between the objects in an allocation group. We present our analysis algorithm to determine when it is safe to fold allocations together and discuss our implementation in V8, an open-source, production JavaScript virtual machine. We present performance results for the Octane and Kraken benchmark suites and show that allocation folding is a strong performance improvement, even in the presence of some heap fragmentation. Additionally, we use four hand-selected benchmarks JPEGEncoder, NBody, Soft3D, and Textwriter where allocation folding has a large impact.